Lab 8: Radiometric Signatures

Lab eight was designed to introduce students to the concept of how objects have unique radiometric signatures. Different mediums of material all have a unique reflective value across all the bands meaning that they can be classified via their radiometric values. While I do not go as far as to automatically classify features in this lab I do go through the process of collecting the radiometric signatures of different types of land cover.

All the data for this lab was provided by the Earth Resources Observation and Science Center, United States Geological Survey

In order to start this process I needed to find features and record their reflectance. I used the spectroradiometer tool included in the Erdas interface to record the signature of about a dozen different feature types such as standing/moving water, vegetation, crops, and urban features. This operation can be done at a smaller, more accurate scale in the field with a physical spectroradiometer but recording the signatures with this program will work just fine for my application.

Figure 1. Radiometric signature of standing water (left window). Notice the comparatively high reflectance of bands 1-3 when compared to bands 4-6. 

Standing water was the first feature which I recorded a radiometric signature. If we look at the signature mean plot, as seen in the above image, we notice that the highest levels of reflectance can been seen in the first three bands of the spectrum; blue being the highest with green and red having slightly lower reflectance. Bands 4-6 represent the infrared colors. In water with a depth greater than 2 meters nearly all infrared light is absorbed by the water while most the blue, green, and, to some degree, red are transmitted. Not very much of the energy is reflected off the surface and the remaining energy that is picked up by the spectroradiometer is due to volumetric reflectance of energy that was previously transmitted through the water.

Figure 2. Radiometric signatures of 12 different features. 

 Standing water is only one of 12 different feature types that I need to record the radiometric signature for. After several minutes of looking around the map I was able to collect all the signatures needed. And because of the different mediums each is made of I was able to see distinct difference in the signatures of the different land cover types (figure 2). When looking at the chart above one can see some of these differences and infer to the reasons why.  Let’s take vegetation for example, it has a higher reflectance of the infrared spectrum than it does the visible. This is because vegetation absorbs much visible light to convert it to energy while it reflects the infrared to prevent damage to proteins within its cells.

Figure 3. Comparing the radiometric signatures of riparian and normal vegetation. 

When interpreting the data it can be difficult to tell differences between feature types that are similar in composition. We can compare riparian (located on a hillsides near rivers) vegetation with regular vegetation (figure 3). All the bands are pretty much the same with the exception of band four; near infrared. Due to the higher water content in the riparian vegetation due to its easy access to water it absorbs slightly more infrared energy than the vegetation that is further from water, meaning it is slightly dryer. The difference of reflectance on band four between the two types is the only significant one in the spectral signature that makes it possible to tell the vegetation types apart. 

Lab 7: Stereoscopy and Orthorectification

Lab seven was designed to introduce students to some to the old and new processes of measuring distances in images, createing a three dimension anaglyphic image, and to orthorectify images. This blog has been separated into three parts, one part for each of the aforementioned topics. All imagery has been provided by my instructor Dr. Wilson.

Part 1: Measuring distances

The first part of this lab was all about measuring distances and areas with a computer and calculating scales and relief displacement with the use of hand written formulas. The first two tasks were to calculate the scale of aerial photographs with the use of a ruler and information given about real word distances in the photograph or about the airplane which took them. The first question was to determine a simple scale ratio of an image using a surveyed distance between two real world points. By measuring this distance on the photo and comparing to the real world data I was able to figure out the scale ratio of the image.

                The second question was slightly more complex. I was to determine the scale ratio of a photo by using both land elevation data (796 FASL) flight elevation data (20,000 FASL) and the focal length of the camera (152mm) which took the photo. Below is an image of the math I completed to come up with the scale (Figure 1).

Figure 1. Sloppy hand writing shows the work done to determine and approximate 1:4000 scale of an aerial photograph. 

Figure 2. Photo used to determine the height of the smokestack. 

The final bit of hand calculations were to determine the height of a smoke stack (labeled “A” in figure 2). The scale of the principle point, scale and altitude of the plane when the photo was taken were supplied. Below in figure 3 one can see the simple hand calculations needed to determine its height.

Figure 3.  More sloppy hand writhing shows the math used to determine the height of a smokestack with the use of an image. 

The next area and perimeter calculations were done using the measure polygon tool in Erdas Imagine. 

Figure 4. Erdas Imagine measure polygon tool in action. 

This tool is pretty simple to use, it is just a matter of placing nodes around the perimeter of the feature to be measured. Once the border of the feature has been outlined and the polygon completed Erdas calculates the shape area and perimeter automatically. The image above shows the halfway point in my progress of drawing a polygon around a lake (Figure 4).

Part 2: Stereoscopy

                For part two of this lab I was required make an anaglyphic aerial image of the Eau Claire Area with Erdas Imagine. This image could then be viewed with polaroid glasses to see a three dimension representation of the city of Eau Claire. In order to complete this operation I used two input files, one was a photo with a one meter spatial resolution and the other was a digital elevation model of the city which had a 10 meter spatial resolution. Using the anaglyph generation tool in Erdas was a very simple process which did not require the placement of any geographic control points or extensive image manipulation.

Figure 5. Screenshot of Erdas Imagine working to create an anaglyph image from an aerial photograph (left) and a digital elevation model. 

Figure 6. Anaglyphic image of UWEC.

After the anaglyphic image of Eau Claire was generated I was able to see some differences in relief in the image (Figure 6). But compared to the places that Stereoscopic images usually portray, Eau Claire is far too flat to have much of an effect when viewed through the glasses.

Part3: orthorectification

The third part of this lab required me to orthorectify two images of the Palm Springs area in California. Both were taken from the SPOT satellite and have a spatial resolution of 10 meters. Neither one of the images have been georrectifed and were in sore need of adjustment. In the image below I overlaid the images in the same viewer and used a horizontal swipe tool to see the difference between the images (Figure 6).

Figure 6. SPOT satellite images of The Palm Springs California area in need of georectification. 

In order to correct these images I first needed to import them into the photogrammetry project manager in Eradas Imagine and define the sensor which took the image. I then had to define what coordinate system, projection, and elevation model the image was is. Now I was ready to start placing geographic control points (GCP). I used an image of the area which had already been orthorectified. 

Figure 7. Adding Geographic Control points to images. Frames on right automatically positioned to GCO area by automatic (x,y) drive. 

After placing the first two GCPs in the correct locations I was able to activate the automatic drive tool to speed up the process of placing additional GCPs (Figure 7). The tool essentially uses the existing data from previous control points to approximate the location of newer points. At first this feature of the program is a time saver but as more points are placd the more accurate the program approximates the location of new ones. After placing an adequate amount of control points on this image I was ready to add elevation data. I used a digital elevation model (DEM) of the area to update the elevation of the GCPs.

Next I added the second image to be orthorectified. I used the same process to set the sensor type and coordinate system as I did to the first image. I then proceeded to add GCPs. This was a fairly quick process seeing as I was using the same GCPs I had placed earlier on the first image and I was able to continue using the automatic drive tool.

Now that that both images have had some GCPs and elevation data added I was able to use another feature of the photogrammetry project called an automatic tie point collection tool to automatically create additional GCPs. When using the tool I set it to make at most 40 more GCPs. After the tool had run there were an additional 25 GCPs which were actually pretty well placed.

Figure 8. Control point #23 was generated automatically with the use of a tie point collection function. Its placement appears to be fairly accurate. 

Finally I am able to perform image resampling. I chose to set the resampling method to bilinear interpolation and set the program to correct both images in tandem.

Figure 9. Images orthorectified in tandem for both horizontal and vertical displacement.

The above image shows the results of the process. Spatially they are pretty accurate but there are some areas where there is a noticeable difference between the locations of the same feature on the two images. This is exaggerated where elevation changes are the highest. The edge of the top overlaid photo is very interesting, it is not straight and bends along with the changes in elevation due to the pixels being resampled to the correct locations.

Lab 6 Geometric Correction

The purpose of this lab was to introduce students to the process of geometric correction of satellite images. For this lab I was required to geolocate a Landsat TM satellite image by the use of a USGS topographic map of the corresponding Chicago Metropolitan area. I was also to do a similar process with an image of an area in eastern Sierra Leone.

 Objective one: Image to map rectification

For the first objective of this lab I was tasked with rectifying a satellite image of the Chicago area with Erdas Imagine by the use of Geographic Control Point pairs. I used a first order polynomial for the geometric model which essentially translates the pixels from the image onto a flat plane. While it is not as accurate as using a higher order polynomial to correct the image it did save some time rendering the finished rectified image (accuracy really wasn’t a huge problem anyways, the original image was not terribly distorted).  After specifying to use the USGS topographic map as the reference image I proceeded to add Geographic Control Points (GCP) in places easily identified in both the satellite and the map. Seeing as I was only using a first order polynomial I needed only three GCPs. For good measure I added a forth to help minimize distortion which may have resulted by not distributing the GCPs evenly throughout the map. The below image (Figure 1) shows multipoint geometric correction window I used to place the GCPs. 

Figure 1. Multipoint geometric correction window. Note the similar location of the geographic control point placement in both the satellite image (Left) and USGS topographic map (Right). 

Initially the GCPs were not placed accurately which resulted in a moderately high Root Mean Square Error (RMS), a number derived from the distance formula to help the analyst to accurately place GCPs. After carefully moving the GCPs on the satellite image to better represent the locations selected on the reference map I was able to decrees the RMS from over 4.0 to ~0.31. Once the RMS was decreased to an acceptable level I was tasked to resample the image using nearest neighbor interpolation. The rectified imaged looked essentially the same as the original image with the exception of the pixels being slightly adjusted.

Objective 2: Image to image registration

For the second portion of the lab I was to correct two similar images of a portion of Sierra Leone. Unlike the image used for objective one, this image was noticeably distorted when compared the reference image. So instead of using a fist order polynomial function I used a third order one. Because of this I had to create at least ten GCPs instead of just three. Figure 2 below shows the interface and the GCPs I placed on the distorted and corresponding reference image.

Figure 2. Multipoint geometric correction window showing distorted image left and geometrically corrected reference image right. Locational data for GCPs displayed in table at bottom of image.

I ended up adding 13 total GCPs and was able to reduce my total RMS error to ~0.17 through careful adjustments of the most poorly placed GCPs. Once finished I was ready to run an interpolation process on the image which would make it geometrically correct. Unlike the first image I used a bilinear interpolation method instead of nearest neighbor. I believe this to be a good choice because, due to the distortion of the original image, nearest neighbor would probably loose data from the image. One the process was finished I was quite surprised with how well the accuracy of the resampled image compared to the reference image. When running a swipe tool to compare them I was not able to discern any spatial difference until I magnified both images enough to see individual pixels. And even then the difference was minimal.

 All data used in this lab provided by:

Satellite images: Earth Resources Observation and Science Center, United States Geological Survey.

Digital raster graphic (DRG): Illinois Geospatial Data Clearing House.

Lab 5: Interpretation of Lidar Data

The learning outcomes for this lab was to become familiar with the manipulation of Lidar Data. The first objective was the introduction of some of the tasks associated with editing raw Lidar data so as to be able to display it within a GIS. Once the data was displayed I learned several simple viewing and conversion techniques to be able to symbolize and understand the data. Finally I was tasked with exporting the data as a raster file. 

Objective 1: Formatting data

At the beginning of this lab I was provided with a point cloud data set which I was able to view in both Erdas Imagine and ESRI ArcMap. Most of the data manipulation was done with ArcMap seeing as it has a slightly better interface than Erdas. But in order to get ArcMap to properly project the data I had to sift through the included metadata file information to find its original datum and unit of measurement for both the horizontal XY axis and Vertical Z axis. After defining this information for the point cloud data by using ArcCatalog I was able to display it within ArcMap.

Objective 2: Viewing the data

Once the data was formatted properly for display in ArcMap I was able to symbolize several aspects of the data. Four common displays are shown below in figure 1, a four panel map showing the same extent some aspects of the University of Wisconsin Eau Claire. The data included all returns with the exception of aspect, which used only the ground returns so as to simplify the data display.

Figure 1. Lidar data symbolized for Elevation, Aspect, Slope, and Contour. 

Elevation is the display of height of the data, in this map the “hotter” colors (reds oranges) are higher than the cooler colors (blues and greens). Slope shows the steepest angles of features as a vibrant red (building walls or trees) and flatter features (rooftops or fields) as green. Aspect is a continuation of Slope but instead of symbolizing the degree of slope it show the direction of the slope. Contour is a display of elevation changes by the use of user defined isoclines. In this case the contour interval was set to five feet. With the index contours set for every five lines.

To help see the how the Lidar interacts with a surface and to display the 3d qualities of a dataset ESRI integrated several featuers into their software, such as the abilitie to take cross sections of an area to see its profile or to even render an area in three dimensions. See the images below (Figure 2-4).

Figure 2. Profile of a railroad bridge using the first return.

Figure 3. Profile view of the South side of Phillips Science Hall on the UWEC campus using the first return. One can see a surprising amount of detail in this image. Note the observatory on the roof of the building and the sport utility vehicle parked several floors directly below it. 

Figure 4. Three dimensional rendering of features of the University of Wisconsin Eau Claire Campus.

Objective 3: Rasterizing Data

For this objective I was tasked with converting the point data from the LAS file to a raster file type. From that raster file I was to make a hill shade image using ESRI software. It was a pretty straight forward process that only involved the use of two tools: “LAS to Raster” and “Hillshade.” The LAS to raster required some parameters to be filled in to work properly but the hill shade tool did not require any special inputs. Below (Figure 5) is a collaborated image of the original LAS file, and the two derived files: raster and hill shade.

Figure 5. A progression of data manipulation from the original LAS point data (first return), to raster, and finally to a hill shade.

Using the same process above I performed a raster conversion and created a hill shade of the same LAS data file as before. The only difference being that I used ground return points instead of first return points (Figure 6).

Figure 6. LAS ground return data converted to raster then hillshaded.

The final part of this lab was to generate an intensity image from the LAS file (Figure 7).

Figure 7. Intensity image produced from LAS data file.

This image was created in a similar fashion as the hill shades above. The difference being that instead of defining the elevation aspect to be converted to raster the intensity strength was used. This shows the most reflective features in the image with lighter tones while the more absorptive features appear darker. 

Lab 4: Introduction to Erdas Imagine

The first 3 labs of this course were devoted to learning the most basic functions of Erdas Imagine. For lab four I was introduced to some of the more complex and interesting functions which this program is capable of. For example I was required to delineate an area of interest from a large satellite image, manipulate images to enhance their ability to be analyzed, and used graphical modeling to distinguish areas of an image where changes in vegetation have occurred. 

Objective 1:  Image Subsetting

                This objective of the lab involves subsetting an image by the use of the subset and chip function of Erdas Imagine. The first step to using this function is defining an area of interest (AOI) from an Image. There are two simple ways that an AOI can be determined: by using an inquire box (figure 1) or by using a georeferenced shapefile (figure 2). While using the inquire box function is simple the shapfile AOI selection method is advantageous because it can display predetermined boundaries easier and is not limited to rectangular shapes. 

Figure 1. Left: original Image. Right: Inquire box subsetted image. 

Figure 2. Left: original image, note polygons over Chippewa and Eau Claire counties. Right: shapefile defined subsetted image. 

Objective 2: Image fusion

                For this objective I was able to improve the spatial resolution of an image by merging a 30 meter reflective image with a 15 meter panchromatic image. The pixel values were determined by the Erdas resolution merge function set to use a multiplicative algorithm and nearest neighbor resampling technique. One can see the differences between all three images below (Figure 3).

Figure 3. Left: resolution merged (Pansharpened) image. Center: original Multispectral Image. Right: Original panchromatic image.

Objective 3: Basic Radiometric Enhancement Techniques

                With this portion of the lab I used a haze reduction function on Erdas to improve the spectral and radiometric clarity of the image. One can see the improvement of contrast between both images below (Figure 4).

Figure 4. Left: original image. Right: Modified, haze reduced image.

 Objective 5: Resampling

                For this objective I was able to resample the image using both nearest neighbor and bilinear interpolation. There is no difference, aside from the increased amount of pixels, between the original image and the one resampled using the nearest neighbor method. There was a much more pronounced difference between the image resampled using the bilinear interpolation technique and the original. The image is more spatially accurate and many features are smoothed out. One can observe these differences below (figure 5).

Figure 5. Left: original image. Center: nearest neighbor resampled image. Right: bilinear interpolation resampled image. 

Objective 6: Image Mosaicking

                For this objective I was tasked with creating several image mosaics. The first mosaics was created using the Erdas function Mosaic Express. While this technique of image merging was easy to set up and quick to render on the computer the final image left much to be desired (Figure 6). There was not a smooth transition between the images making it quite obvious where the original image’s borders were. One advantage of the function is how the radiometric changes increased the contrast of the output image.  

Figure 6. Left: original images. Right: Erdas Mosiac Express image. 

Objective 7: Image differencing

                This section of the lab was designed to introduce students to the concept of image differencing, a technique used to see changes in one place over time. After opening two images of the same area that were taken 20 years apart I used the Erdas Two Image Functions to perform a binary change detection between band 4 on the images. After running the process no change was apparent in the new image so I was required to manipulate the histogram portion of the image metadata so as to determine the areas brightness changed the most (Figure 8). 

Figure 8. Binary change image histogram.

 In order to better demonstrate the change between the images I was tasked to use Erdas Model Maker to put together a basic model for determining the change in brightness vales between the images. After running a two basic operations with the model maker I was able to isolate the pixels in the image which had changed the most (Figure 9).  

Figure 9. Left: subtracted values between 1991 and 2011 images. Right: Highlighting of pixels that had the most change in brightness values.

 The final operation for this lab is to display the brightness values from the last step on a contrasting base map. I used ESRI ArcMap to complete this task. Unfortunately due to time constraints I was not able to get it to look as nice as I would have liked to (Figure 10).

Figure 10. Completed map showing binary change.