A LiDAR Odyssey in Earth Observation

ISPRS Laserscanning `09 invited talk is online.

from

"Pierre H. Flamant

Laboratoire de Météorologie Dynamique,

Institut Pierre Simon Laplace

École Polytechnique"

Very interesting and enjoyable pdf about Earth`s atmospheric LiDAR research.

http://laserscanning2009.ign.fr/download/LS09_Invited_talk_PHF.pdf

Private "laser shield"

I don`t believe it....that must be a very interesting system!




http://www.timesonline.co.uk/tol/news/world/europe/article6841380.ece

Riegl VZ 400

Let me introduce the one of the best TLS system... I wrote already about this scanner. Last winter I met with Riegl VZ-400, you can control it with an iphone....amazing stuff...




"The new RIEGL VZ-400 3D Terrestrial Laser Scanner provides high speed, non-contact data acquisition using a narrow infrared laser beam and a fast scanning mechanism. High-accuracy laser ranging is based upon RIEGL’s unique echo digitization and online waveform analysis, which allows achieving superior measurement capability even under adverse atmospheric conditions and the evaluation of multiple target echoes. The line scanning mechanism is based upon a fast rotating multi-facet polygonal mirror, which provides fully linear, unidirectional and parallel scan lines. The RIEGL VZ-400 is a very compact and lightweight surveying instrument, mountable in any orientation and even under limited space conditions."

www.riegl.com/products/terrestrial-scanning/produktdetail/product/scanner/5/

www.riegl.com/uploads/tx_pxpriegldownloads/10_DataSheet_VZ400_10-09-2009.pdf

Full-Waveform filtering

Next generation scanners are integrated with full-waveform data acquisitions properties. Every single return pulse is documented. We can research the vertical conditions of the forest areas. Tree, bush, grass...different data class.


Interesting paper about full-waveform datasets....

INTEGRATION OF FULL-WAVEFORM INFORMATION INTO
THE AIRBORNE LASER SCANNING DATA FILTERING PROCESS
Y. -C. Lin and J. P. Mills

"ABSTRACT
Terrain classification of current discrete airborne laser scanning data requires filtering algorithms based on the spatial relationship between neighbouring three-dimensional points. However, difficulties commonly occur with low vegetation on steep slopes and when abrupt changes take place in the terrain. This paper reports on the integration of additional information from latest generation full-waveform data into a filtering algorithm in order to achieve improved digital terrain model (DTM) creation. Prior to a filtering procedure, each point was given an attribute based on pulse width information. A novel routine was then used to integrate pulse width information into the progressive densification filter developed by Axelsson. The performance was investigated in two areas that were found to be problematic when applying typical filtering algorithms. The derived DTM was found to be up to 0.7 m more accurate than the conventional filtering approach. Moreover, compared to typical filtering algorithms, dense low vegetation points could be removed more effectively. Overall, it is recommended that integrating waveform information can provide a solution for areas where typical filtering algorithms cannot perform well. Full-waveform systems are relatively cost-effective in terms of providing additional information without the need to fuse data from other sensors.

CONCLUSIONS
This study set out to investigate whether information derived from the latest generation full-waveform, small-footprint airborne laser scanning data could improve digital terrain modelling. A novel routine was designed to integrate waveform information into a commonly used filtering algorithm. The preliminary results have demonstrated that integrating pulse width information can provide a solution for areas where conventional filtering algorithms cannot perform well. On the top of an artificial mound, points rejected (Type I error) by a typical filtering algorithm can be correctly included in the developed routine. More low vegetation can also be correctly removed. However, rough or steep terrains with low vegetation cover, as well as forest terrain, still require further investigation and detailed validation. In addition, although identifying vegetation points becomes easier with the help of waveform information, it may be the case that in some densely vegetated areas insufficient “true” ground points exist for high-resolution DTM generation. In such cases, it might still be better to assume that the lowest point within a specified or adaptive window size is a ground point. The performance of existing filtering algorithms depends on the type of landscape (Sithole and Vosselman, 2004). Such approaches may require that users determine which type of landscape is being processed and then specify optimal parameters. As demonstrated in this paper, by using waveform information it is possible to automatically determine the landscape characteristics and then use the optimal parameter set for that specific landscape type. This will improve the automation of filtering procedures since less effort is required by users. Moreover, compared to ALS intensity values, pulse width information is easier to apply to different surveys since neither prior calibration or normalization procedures are required. Using full-waveform ALS data provides valuable physical and geometric information simultaneously. Such systems are relatively cost-effective in terms of providing multiple-information without the need to fuse data from other sensors."

Pathway detection with LiDAR

Mapping of pathways is very difficult. We don't know exactly, where are the hidden ways in the forest, because we don't see through the trees on the aerial photos. :)....but, with LiDAR datasets that kind of mapping is possible.....important information is this, military reasons etc..




PATHWAY DETECTION AND GEOMETRICAL DESCRIPTION FROM ALS DATA IN
FORESTED MOUNTANEOUS AREA
Nicolas David, Cl´ement Mallet, Thomas Pons, Adrien Chauve, Fr´ed´eric Breta


"ABSTRACT:
In the last decade, airborne laser scanning (ALS) systems have become an alternative source for the acquisition of altimeter data. Compared to high resolution orthoimages, one of the main advantages of ALS is the ability of the laser beam to penetrate vegetation and reach the ground underneath. Therefore, 3D point clouds are essential data for computing Digital Terrain Models (DTM) in natural and vegetated areas. DTMs are a key product for many applications such as tree detection, flood modelling, archeology or road detection. Indeed, in forested areas, traditional image-based algorithms for road and pathway detection would partially fail due to their occlusion by the canopy cover. Thus, crucial information for forest management and fire prevention such as road width and slope would be misevaluated. This paper deals with road and pathway detection in a complex forested mountaneous area and with their geometrical parameter extraction using lidar data. Firstly, a three-step image-based methodology is proposed to detect road regions. Lidar feature orthoimages are first generated. Then, road seeds are both automatically and semi-automatically detected. And, a region growing algorithm is carried out to retrieve the full pathways from the seeds previously detected. Secondly, these pathways are vectorized using morphological tools, smoothed, and discretized. Finally, 1D sections within the lidar point cloud are successively generated for each point of the pathways to estimate more accurately road widths in 3D. We also retrieve a precise location of the pathway borders and centers, exported as vector data.

CONCLUSION
A full workflow for the pathway detection on mountainous area, from raw ALS data to vector database objects, have been proposed. With the increasing use of ALS data for DTM generation, such workflow should enabled to decrease the data acquisition cost for mapping institute. The detected pathways could also be used both for improving DTM generation and as features for strip adjustment and registration. The results show the feasibility of generating and updating pathway databases from ALS data, but their quality is still insufficient to be used on a production context for mapping agencies. In order to tackle the mentioned issues, it has been draw perspectives to improve robustness and automaticity of pathway detection."

Geography and LiDAR II.

Interesting paper from Australia....
mapping of rivers from LiDAR datasets is very popular too....flood risk management.....

OPERATIONAL MAPPING OF THE ENVIRONMENTAL CONDITION OF RIPARIAN
ZONES OVER LARGE REGIONS FROM AIRBORNE LIDAR DATA
K. Johansen, L. Arroyo and S. Phinn

"ABSTRACT:
Riparian zones maintain water quality, support multiple geomorphic processes, contain significant biodiversity and also maintain the aesthetics of the landscape. Australian state and national government agencies responsible for managing riparian zones are planning missions for acquiring remotely sensed data covering the main streams in Victoria, New South Wales, and parts of Queensland and South Australia. The objectives of this paper are to: (1) assess the ability of LiDAR data for mapping the environmental condition of riparian zones; and (2) provide specifications for capturing and analyzing the LiDAR data for riparian zone mapping at large spatial extents (> 1000 km of stream length). LiDAR derived digital elevation models, terrain slope, intensity, fractional cover counts and canopy height models were used for mapping riparian condition indicators using simple algorithms and more complex objectoriented image analysis. The results showed that LiDAR data can be used to accurately map: water bodies (producer’s accuracy = 93%); streambed width (Root Mean Square Error (RMSE) = 3.3 m); bank-full width (RMSE = 6.1 m); riparian zone width (RMSE = 7.0 m); width of vegetation (RMSE = 5.6 m); plant projective cover (RMSE = 12%); vegetation height classes (vertical accuracy < r2 =" 0.40)."> 100,000 km of stream length."

Geography and LiDAR

researches at forest areas are a very popular part of the LiDAR know-how....

One of my favorite paper is from ISPRS Laserscanning '09.....very useful and innovative ideas, we need that kind of work, and not the discussions of RMS faults....how can I correct another mm-s....


A DECIDIOUS-CONIFEROUS SINGLE TREE CLASSIFICATION AND INTERNAL
STRUCTURE DERIVATION USING AIRBORNE LIDAR DATA
C. Ko, G. Sohn, T. K. Remmel

"ABSTRACT:
This project has two main purposes; the first is to perform deciduous-coniferous classification for 65 trees by using the leaf-on single flight LiDAR data. It was done by looking at the geometrical properties of the crown shapes (spherical, conical or cylindrical), these shapes were developed by a rule-driven method Lindenmayer Systems (L systems). Two more parameters that are data driven (convex hull analysis and buffer analysis) were developed to further capture the geometrical differences between deciduous and coniferous trees. Proposed methods are scale independent and arithmetically simple, they were developed simply by looking at the geometrical differences between the two types of trees. The classification rate was cross-validated and trees are 85% - 88% correctly classified. The second part of the project is to derive the internal structures of the LiDAR tree according to the results obtained from the first part. Internal structures include bole and branches; the location and orientation of the bole was done by connecting the geographic centres of horizontal slices of the tree. The branches were derived by k-means clustering techniques, different types of trees will yield a different type of branching structures for better visualization.

DISCUSSION / CONCLUSION
There are two major types of LiDAR systems for research and commercial use, full waveform and discrete returns. This paper has used only the discrete returns of the range data, and by studying the geometry of the crown shape properties, we classified the different crowns into two major classes, deciduous and coniferous. From the classified results, bole and branching structures were reconstructed according to the type of tree. The shape of the tree crown is inherited in the gene (adaptation) and therefore a certain species will have the similar crown shape and branching structures. The other factor affecting crown shape and branching structure is the growing strategies, which is adopted by the growing neighbour environment and those are more difficult to model (Horn, 1971). As a result, crown geometry is believed to be an important piece of information for species classification. By using just the three geometrical shapes (sphere, cone and cylinder), results were improved from 65% to 67% when the outliers were removed. If other parameters are included (area to volume ratio of convex hull and point to polygon buffering analysis), results were improved from 85% to 88%. Using crown shapes to classify trees is an intuitive method, but in this study it did not show promising results. By looking at the other geometrical properties, the results for classification increased considerably. Although different from what was expected, it is still believed crown shape and internal structure are good indicators for classifying trees, and future studies should be conducted in this direction. This method of classification is quite simple to produced and arithmetically easy. Tree bole and branches reconstructions are for visualization, but can also be used to study growth behaviour and to provide insight regarding why trees grow in a particular directions. These results are useful in many types of studies. For example, it can be used to study the potential hazards of a tree growing into structures, by classifying trees into deciduous and coniferous provide a better growth estimates."

Outcrop Modeling

I like geography...our "terra" is so complex system, I think we shold be happy, that we here live....I open a beer....:)





Second paper about photogrammetry and LiDAR:

TERRESTRIAL LASER SCANNING COMBINED WITH PHOTOGRAMMETRY FOR
DIGITAL OUTCROP MODELLING
S. J. Buckley, E. Schwarz, V. Terlaky, J. A. Howell, R. W. C. Arnott


"ABSTRACT:
The integration of 3D modelling techniques is often advantageous for obtaining the most complete and useful object coverage for many application areas. In this paper, terrestrial laser scanning and digital photogrammetry were combined for the purposes of modelling a geological outcrop at Castle Creek, British Columbia, Canada. The outcrop, covering approximately 2.5 km2, comprised a smooth, scoured surface where recent glacial retreat had left the underlying sedimentary rocks exposed. The outcrop was of geological interest as an analogue to existing hydrocarbon reservoirs, and detailed spatial data were required to be able to map stratigraphic surfaces in 3D over the extent of the exposure. Aerial photogrammetry was used to provide a 2.5D digital elevation model of the overall outcrop surface. However, because the sedimentary strata were vertically orientated, local vertical cliffs acted as cross-sections through the geology, and these were surveyed using a terrestrial laser scanner and calibrated digital camera. Digital elevation models (DEMs) created from both methods were registered and merged, with the fused model showing a higher fidelity to the true topographic surface than either input technique. The final model was texture mapped using both the aerial and terrestrial photographs, using a local triangle reassignment to ensure that the most suitable images were chosen for each facet. This photorealistic model formed the basis for digitising the geological surfaces in 3D and building up a full 3D geocellular volume using these surfaces as input constraints. Because of the high resolution and accuracy of the input datasets, and the efficacy of the merging method, it was possible to interpret and track subtle surface separations over the larger extents of the outcrop.

CONCLUSIONS
Terrestrial laser scanning and digital aerial photogrammetry were combined to create a digital elevation model of the Castle Creek outcrop, British Columbia, Canada. The integration of the two techniques proved to be essential to capture both the large outcrop surface and the near-vertical cliff sections which were essential for being able to recreate the 3D orientation of geological surfaces. Use of surface matching allowed the aerial photogrammetric DEM to be accurately registered, without the problems of collecting a conventional photocontrol point arrangement in a rugged and remote area. Texture mapping with aerial and terrestrial images resulted in a photorealistic model that could be used by geologists for interpretation, education and quantitative analysis. This model demonstrated the application of geomatics for geological outcrop analogue modelling, allowing the spatial accuracy and resolution to be enhanced. A geocellular volume was created from digitised features, which will be used by geologists to improve the geological understanding of the Castle Creek outcrop."

Integration Approach of Photogrammetric and LiDAR

There are a lot of discussions, photogrammetric or LiDAR....I think, for this question difficult to find the answers, different projects, different solutions....maybe photogrammetric and LiDAR...we must use every opportunity to complete our work...


Here is a paper about integration techniques:

NEW INTEGRATION APPROACH OF PHOTOGRAMMETRIC AND LIDAR
TECHNIQUES FOR ARCHITECTURAL SURVEYS
F. Nex, F. Rinaudo

"ABSTRACT:
In the last few years, LIDAR and image-matching techniques have been employed in many application fields because of their quickness in point cloud generation. Nevertheless, these techniques do not assure complete and reliable results, especially in complex applications such as architectural surveys: laser scanning techniques do not allow the correct position of object breaklines to be extracted while image matching results require an accurate editing and they are not acceptable for bad-textured images. For this reason several authors have already suggested overcoming of these problems through a combination of LIDAR and photogrammetric techniques. These works considers the integration as the possibility to share point clouds generated by these techniques; however, a complete and automatic integration, in order to achieve more complete information, has never been implemented. In this paper, a new integration approach is proposed. This integration is focused on the possibility of overcoming the problems of each technique. In this approach LIDAR and multi-image matching techniques combine and share information in order to extract building breaklines in the space, perform the point cloud segmentation and speed up the modelling process in an automatic way. This integration is still an ongoing process: the algorithm workflow and first performed tests on real facades are presented in this paper, in order to evaluate the reliability of the proposed method; finally, an overview on the future developments is offered.

CONCLUSIONS AND FUTURE DEVELOPMENTS
The performed tests have allowed a first evaluation to be made of the potentiality of the proposed method, even though this analysis is not complete yet and further tests and changes have to be defined. Nevertheless, the results have already shown the reliability of the algorithm. In general, the results depend on the image taking configuration: almost normal case images are weak in the matching of edges parallel to epipolar lines. This problem could be overcome by just using more than three images and an ad hoc taking geometry. Images acquired at different heights allow epipolar lines with different direction to be obtained (Figure 9); instead, convergent images (more than 20°) could be subject to problems due to affine deformations. The taking distance should be chosen according to the degree of detail requested in the survey: in general, a 15 m distance can be considered the maximum for architectural objects to be drawn at 1:50 scale. The algorithm has shown that it can achieve good results for repetitive patterns, particularly if more than three images are used. The number of mismatches is usually low and decreases as the number of images increases. Glass, however, must always be deleted from all the images, in order to avoid mismatches. Dense point clouds in the LIDAR acquisition are not strictly necessary during the matching process while they are necessary instead in the filtering of the geometric edges. In fact the algorithm has shown some problems because of the presence of shadows close to the geometric breaklines; a more dense point cloud could overcome this problem. Furthermore, rounded edges are difficult to model as it is difficult to identify the position of the breakline in the image: in this situation, the algorithm does not allow good results to be obtained. It is expected that the geometric precision in the edge matching will be increased by implementing a Least Square Matching (LSM). A first step will be to perform a Multi-Photo LSM of dominants points; then it is planned to carry out an LS B-Snake matching (Zhang, 2005) which could slightly improve the quality of extracted edges. A great advantage in the traditional point cloud segmentation will be obtained from the extracted edges. The segmentation will be guided by edges that define the boundaries of each façade portion and fix a constraint in the region growing algorithm. In this way it is expected to make the modelling procedures easier."

Rocket Scientists Shoot Down Mosquitoes With Lasers

For the first read I didn't believe it...

http://online.wsj.com/article/SB123680870885500701.html

"Each time a beam strikes a bug, the computer makes a gunshot sound to signal a direct hit." - wow! Excerpt:

http://www.physorg.com/news156423566.html

Rome built in a day

Okay, this is just uebercool, but I couldn't find any information about the precision of these models.

They look nice, anyway.

http://grail.cs.washington.edu/rome/

http://www.physorg.com/news172241228.html

Always knowing where is north

This little unit makes you able to know where is north all the time:

There is something (especially in male humans) called senso dell'orientamento (in Italian), which does exactly that. Knowing where is north.

However, it's quite easy to follow the directions, if you enter a square building. But there are situations (more than you can imagine!) where your 'direction-sense' will betray you.

Normally you don't even need that sense, so you'll just ignore the failure, or wouldn't even notice it.

But if you drive a few miles on a serpentine road on a shady day, or you enter the underground, and travel a few stations, you'll be surprised.. With this machine you could be more aware of the direction.

However, a few years back I've already read about a similar experiment, and the result was the same: the experimenter was surprised. (So this team just reinvented the compass.)

I'm sure that these kind of augmented reality (or augmented perception) devices wont be mainstream soon, but there are some fields, where it could be useful.

(For instance: navy, rescue teams, pilots, hikers, and maybe geographers.)

http://hplusmagazine.com/articles/enhanced/my-new-sense-organ

MilkScanner

This one is tough: the milkscanner.
Really great idea, however, it's hard to imagine its industrial use due to lack of precision. But it's really cool, and compelling that 3D scanning is available to even larger groups.
http://milkscanner.moviesandbox.net/
http://www.instructables.com/id/Milkscanner-V1.0/

Permanent cloud and rain over cartoon city

This poor little lakeside town has clouds over it every time...

(I've been checking it for the last few months.)

Hopefully I'll be there in a few weeks, with a bicycle, and a few friends.

(So I'll check that cloud in real life as well.. :)

Logo II.

This is my next try:

However, I'm not satisfied with that... I simply wanted to be it a little bit less linear, but now it looks falling apart. The double border around seems departed now.

Being short of time, I'll only upload the differences in the code.

Now I'm using this line, to determine the difference horizontally:

int diffWidth = Math.min(i, width - i - 1)/2;

This implies, that the gradient will be somewhat smaller horizontally than vertically. (Half of it..)

A small problem with this is, that it doubles width of the vertical borderlines. I'll fix that next week, it's not that cool now.

The other change was, the nonlinearity.

Instead of the modulo function, now I'm using a whole new function to determine, what heights will be grey:

if(isGrey(diffMin)) grey = true;

which is equal to:

grey = isGrey(diffMin);

And the function is:

public static boolean isGrey(int diff)

{

int counter = 6;

int sum = counter;

while(sum <= diff)

{

if(sum == diff)return true;

counter+=4;

sum += counter;

}

return false;

}

Well, the small problem with this is, that it computes every possible height every time. It's not a big deal, since it runs in a few milliseconds, but it would be faster, if I first create a HashSet of integer values, put in the first few values (in this case: 6, 13, 21, 30, 40, 51, 62, ...), and search in that HashSet every time. Maybe I'll do that later.

(However, that would be better even because I could set the heights of the lines manually, without writing an algorithm. For such a small application it would be maybe better.)

Okay, that's it for now, I'll come with a new logo next week.

LiDAR intensity correction

The second LiDAR intensity paper ..... here we find new information about topographic and distance effects.

Next time I write about photogrammetric and LiDAR integration opportunities. Very interesting research area!


TOPOGRAPHIC AND DISTANCE EFFECTS IN LASER SCANNER INTENSITY
CORRECTION
S. Kaasalainen, A. Vain, A. Krooks, A. Kukko

"ABSTRACT:
The effect of incidence angle on the intensity of laser backscatter has been studied in photonics and optics, but the applications of these results to remote sensing of natural land targets are limited, as well as the availability of experimental validation data for airborne and terrestrial laser scanning, where the incidence angle correction using the Lambertian scattering law is common. We have investigated the role of topographic (incidence angle) and distance effects in the radiometric calibration of monostatic terrestrial and airborne laser scanner data. Our results show that the Lambertian (cosine) correction is practically valid at incidence angles up to 20º, whereas at greater angles of incidence, the accuracy of data is still very limited to estimate the performance of any correction method. We also discuss the mixed effects of distance and target reflectance on terrestrial laser scanner intensity calibration, for which the number of applications is constantly increasing. As there are differences in the intensity detectors of different instruments, it is important that the effects of distance and target reflectance are well defined before using any terrestrial laser scanner in intensity
measurement.


SUMMARY

At the current levels of accuracy of ALS intensity data, the cosine correction of the incidence angle effect works reasonably well for most surfaces, even in the cases that the surface could not be approximated to be Lambertian. Together with earlier results (Kukko et al., 2008), it can be concluded that for most targets the incidence angle effect is practically within the error limits of the data at incidence angles up to about 20°. The computational cosine effect is about 6% in this range, which is well in the error limits of the intensity data produced by current ALS instruments (this is clearly seen in Fig. 5 and Kaasalainen et al., 2009a). It must also be taken into account that some surfaces may present a specular reflection at 0° (normal incidence), which may cause a peak in the intensity at this angle. More data with better accuracy than that provided by the current airborne scanners would be needed in cases where the incidence angle is greater than 20°, to be able to distinguish any difference between surfaces for which the Lambertian approximation does or does not work adequately. The intensity and distance effects in TLS are mixed, at least for some scanners. A further study on distance effects in TLS is in progress, with a comparison with data from other instruments (Kaasalainen et al., 2009b). This is particularly important in the applications where TLS are used in mobile (vehicle based) mapping systems and other (stationary) applications, where the distance of the target varies in a large scale. It is important that the detector effects are well known (to provide a correction scheme) before using any terrestrial laser scanner in intensity measurement."

Nanoplasmonics

http://www.technologyreview.com/blog/arxiv/24093/

NEWS: Leica ScanStation C10 - the next-generation Laser Scanner

"Heerbrugg, — September 07, 2009 — Leica Geosystems announces the completely new Leica ScanStation C10, the company’s biggest leap forward yet in laser scanners for as-built and topographic surveys....."

I never worked with Leica TLS system, this scanner seems very improved. But....I think without waveform analysis I don`t want to buy it... :)

One of the most impressive TLS today: Riegl VZ-400 (later new topic about this system)

http://www10.giscafe.com/nbc/articles/view_article.php?section=CorpNews&articleid=735825
http://hds.leica-geosystems.com/en/79411.htm

Normalization of LIDAR intensity

There are a lot of research today about laser intensity, because we find a huge potential at Lidar intensity datasets. These information could help by different processes, like segmentation, classification or visualization. All of lidar applicatnions based on x,y,z parameters, and I think, in the future, we will use this extra information (i=intensity), which could mean 4th dimension of lidar.

Normalization of intensity is a very useful direction, here is a new paper:

NORMALIZATION OF LIDAR INTENSITY DATA BASED ON
RANGE AND SURFACE INCIDENCE ANGLE
B. Jutzi, H. Gross

" ABSTRACT:
The analysis of airborne laser scanner data to extract surface features is of great interest in photogrammetric research. Especially for applications based on airborne measurements, where the intensity is crucial (e.g. for segmentation, classification or visualization purposes), a normalization considering the beam divergence, the incidence angle and the atmospheric attenuation is required. Our investigations show that the same material of a surface (e.g. gabled roof) yields to different measured values for the intensity. These values are strongly correlated to the incidence angle of the laser beam on the surface. Therefore the intensity value is improved with the incidence angle derived by the sensor and object position as well as its surface orientation. The surface orientation is estimated by the eigenvectors of the covariance matrix including all object points inside a close environment. Further the atmospheric attenuation is estimated. The adaptation of vegetation areas is disregarded in this study. After these improvements the intensity does no longer depend on the incidence angle but may be influenced by the material of the object surface only. For surface modelling the Phong model is introduced, considering diffuse and specular backscattering characteristics of the surface. A measurement campaign was carried out to investigate the influences of the incidence angle on the measured intensity. By considering the incidence angle and the distance between sensor and object the laser data captured from different flight paths (data stripes) can be successfully fused. In our experiments it could be shown that the radiometric normalization of the intensity for the investigated areas are improved.


DISCUSSION AND CONCLUSION
For assessing the normalized intensity values nearly
homogenous regions have been selected interactively. The
variation parameter is selected as measure for the comparison of
the values before and after normalization. Mean and standard
deviation of this measure over all regions decreases by the
normalization, especially if all flights are included. For pulsed
laser systems a strong intensity variation could be observed. The
intensity inside a region shows a high variance even for a
constant incidence angle. This may caused by material features
or local surface effects. For nearly all regions the results for the intensity have been improved, even with region disturbances on the roofs like chimneys. The Lambertian model fits the investigated surfaces well. For specular reflectance based on the Phong model no significant improvements could be derived. This might depend on the diffuse backscattering characteristic of the material. Further investigations for this study were not possible because only one data set with surfaces of a single material with different orientations was available. For terrestrial laser data enhanced results can be expected, with a lower variance of the intensity, due to a better signal-to-noise ratio for the measured data. This paper proposes a general approach for intensity normalization considering diffuse and specular scattering characteristics of the surface. This assumption should be proved in future by investigating reference targets where the
backscattering characteristic is known or could be measured by reference measurements."

Lasers Generate Underwater Sound

"Efficient conversion of light into sound can be achieved by concentrating the light sufficiently to ionize a small amount of water, which then absorbs laser energy and superheats. The result is a small explosion of steam, which can generate a 220 decibel pulse of sound. Optical properties of water can be manipulated with very intense laser light to act like a focusing lens, allowing nonlinear self-focusing (NSF) to take place. In addition, the slightly different colors of the laser, which travel at different speeds in water due to group velocity dispersion (GVD), can be arranged so that the pulse also compresses in time as it travels through water, further concentrating the light. By using a combination of GVD and NSF, controlled underwater compression of optical pulses can be attained."

http://www.nrl.navy.mil/pressRelease.php?Y=2009&R=63-09r

Messing with our new logo

I'm trying to create a new background for the header of our blog.
You can see the current result here:

It looks quite nice, like the countour-map of a long pyramid-like structure.

This is how I created this one: (in lack of 1337 photoshop sk1lls:)

I created a 24-bit bmp, with the width of 640 and height of 96. I needed this only to create the 54 byte header of my bmp file, so this actual bmp file was blank white. (This file has been saved as 'start.bmp'.)

In this small piece of java code the header will be copied to 'end.bmp':

Reader r = new FileReader("start640.bmp");

BufferedReader br = new BufferedReader(r);

char[] cbuf = new char[256];

int l = 54;

br.read(cbuf, 0, l);

br.close();

FileWriter fw = new FileWriter("end.bmp");

fw.write(cbuf, 0, l);

(Be aware, that file operations need to be in a try-catch block!)

After this, with my brand new FileWriter, I'll continue to fill up the contents of my bmp file. To do that, we need to know, that each pixel will need 3 bytes there, the so called RGB-code. In our case these 3 bytes will be equal, since we need grey lines.

First we have to put down the bottom line pixels, then the above, and so on, until reaching the top. To create the contour, I used a really simple algorithm: first I compute the distance from the border, and I'll color every seventh line, or 'height'. (Just like in a contour-map.)

This is the code:

int width = 640;

int height = 96;

for(int j = 0; j != height; j++)

{

for(int i = 0; i != width; i++)

{

boolean grey = false;

int diffHeight = Math.min(j, height - j - 1);

int diffWidth = Math.min(i, width - i - 1);

int diffMin = Math.min(diffHeight, diffWidth);

if(diffMin % 7 == 2) grey = true;

char colour = 255;

if(grey)colour = 210;

cbuf[0] = colour; cbuf[1] = colour; cbuf[2] = colour;

fw.write(cbuf, 0, 3);

}

}

fw.close();

Simple, isn't it?

When I'll have the time, I'll try to improve the look of this stuff by using a non-linear function, or by random terrain.

(By the way after setting the code color to blue, I was not able to use the line:

for(int i=0; i {smallerthan} max; i++){} // where {smallerthan} means that sign

cause there were some html errors with the styles. How comes this, google?!

I had to modify the 'less than' sign to 'not equals',

and I'm not able to modify the color of the above line...)

NETWORK SNAKES and LiDAR

I think, there is a lot of potential in laser intensity information. Now we use at the most applications the X,Y,Z parameters. We must collect more experiences about LiDAR intensity, because this extra information is very dependent from different properties (material, topography, laser footprint, flight direction....more about these later).

Very unique paper about the feature detection with help of intensity information:


ADAPTATION OF ROADS TO ALS DATA BY MEANS OF NETWORK SNAKES
J. Goepfert, F. Rottensteiner

"ABSTRACT:

In the Authoritative Topographic Cartographic Information System (ATKIS®), which is the main public topographic data base inGermany, the heights and the 2D positions of objects such as roads are stored separately in the digital terrain model (DTM) anddigital landscape model (DLM). However, for many applications a combined visualization and processing of these two data sets is useful, leading to the demand for a 3D representation of the objects. For this purpose an integration of the height and position is essential. However, discrepancies exist between the DLM and the DTM due to different kinds of acquisition, processing, and modeling. This inconsistency has to be solved within the integration algorithm. Airborne Laserscanning (ALS) is used to acquire the height information for the DTM. Therefore, intensity values of the ALS echoes, which contain the reflectance properties of the lluminated objects, as well as object heights can be exploited in addition to the terrain height. However, the ALS data contain the information related to the DLM-objects only implicitly. Usually, features, for example edges or distinctive points, have to be extracted which can be connected to DLM-objects to realize the integration process. In this paper the matching of the data sets is realized by network snakes which are able to use the implicit ALS information about the position of the DLM-objects without the feature extraction step. The initialization of the contour with the vector data enables the use of ALS data as image energy for an iterative optimization process. Examples are given which apply the concept of network-snakes for the adaptation of a road network to ALS data taking advantage of the prior known topology.

CONCLUSION:

In this paper an active contour approach is applied to the
adaptation of a road network to ALS data. Even with a simple
definition of the image energy by combining ALS height and
intensity information promising results could be achieved.
Based on the exploitation of the topology of given initialisation
the snake converges to the desired position in comparison to the
ground truth. Contour parts connected by nodal points support
each other during the optimisation process.
The introduced algorithm offers many possibilities regarding
the integration of object knowledge for future research. For
example, constraints about the shape of the objects (parallelism
of road edges, slope constraints) or relations to other ATKIS
objects can be incorporated in the initialisation as well as in
additional energy terms. Furthermore, a more sophisticated
formulation of suitable image energies concerning different
vector objects can improve the applicability of the method.
Additionally, the algorithm can be extended to other DLM
objects having a relation to features in ALS data such as rivers.
Subsequently, all the adapted objects provide a dense network
of shift vectors which can be used in addition to prior accuracy
knowledge in order to improve the consistency of the DLM and
ALS data."

Image energy from a combination of intensity and height values (green: ground truth; blue: initialisation; red: final position)

ISPRS Workshop Laserscanning 2009 I.

Hallo everybody,

Wednesday finished the ISPRS Laserscanning 2009 in Paris.

Thank you, congress was very professional!

160 people were from 25 countries at the workshop. 31 presenters, extra 4 company presentations and 27 posters were in the program.

31 presenters were from 13 countries. Iran and Greece cancelled.

(Country, number of oral presentation)

Austria	3
Canada	3
Finland	4
France	4
Germany	6
Greece	1
Iran	1
Italy	2
Japan	1
Netherlands	3
Swiss	1
Spain	1
UK	1

Two areas (forestry, urban) are very popular in the LiDAR researches, where the newest papers were presented on the week.

I think, the most interesting topics were:

Automatic classification and feature extraction

TLS calibration

Photogrammetry and LiDAR combination

Analysis and visualisation with Voxel

Usage of LiDAR Intensity information

I want to write more details on the next days about these topics and the papers.