Miscellaneous Image Functions I
Goal
In the realm of remote sensing, many functions can be used to better interpret or display remote sensing images. A lab was used to introduce students to a few of these functions. The lab taught students how to delineate a study area from a larger satellite image scene and how to link a satellite image in Erdas Imagine 2013 to Google Earth. Students also examined how spatial resolution of satellite images can be optimized for visual interpretation. Some radiometric enhancement techniques in optical images were introduced as well as various methods of resampling of satellite images. The lab provided valuable skills in image pre-processing.
In the realm of remote sensing, many functions can be used to better interpret or display remote sensing images. A lab was used to introduce students to a few of these functions. The lab taught students how to delineate a study area from a larger satellite image scene and how to link a satellite image in Erdas Imagine 2013 to Google Earth. Students also examined how spatial resolution of satellite images can be optimized for visual interpretation. Some radiometric enhancement techniques in optical images were introduced as well as various methods of resampling of satellite images. The lab provided valuable skills in image pre-processing.
Methods
Image Subsetting
Image subsetting is used to delineate a region of interest from a larger satellite image scene; this can be thought of as using a cookie cutter. This process is necessary because many times a study area is significantly smaller or not the exact shape of an image scene.
Subset Using an Inquire Box
The first step in subsetting using subset & chip is to import the satellite image into the Erdas Imagine 2013 software. For this exercise, the image used as eau_claire_2011.img. Once the image has been imported, an inquire box must be created. This is done by right clicking on any area within the satellite image. The inquire box is displayed in Figure 1. It is a white box that can resized and moved.
The inquire box must be set to cover the entire area desired for the subset. This can be accomplished by placing the cursor inside the Inquire Box, holding down the left mouse button while dragging the Inquire box to reposition over the study area. For this exercise, the city of Eau Claire, Wisconsin was the study area. When the area was sufficiently covered by the Inquire Box, "apply" must be clicked.
After the Inquire Box was set to cover the study area, the Subset & Chip tool within the raster toolset was clicked followed by the Create Subset Image tool which automatically populates the input image file to the current image in the viewer. To finish the process of subsetting with an Inquire Box, an output location must be set. The From Inquire Box parameter must be clicked to run this tool. This brings the coordinates of the image area covered by the inquire box into the subset interface. All other parameters within this tool interface may be left as default. Clicking OK creates a subset image. A model window will appear; once the model has successfully run, the window can be dismissed. A new subset image of the study area (Eau Claire, Wisconsin) has been created, but must be imported into the Erdas Viewer. Figure 2 displays the original eau_claire_2011.img as well as the newly created subset image.
There are limitations to this method of subsetting. The study area may not be in the shape of a rectangle or square (the only shape option using an Inquire Box). The second method of subsetting is quite useful to avoid this limitation.
Subset Using an Area of Interest Shapefile
The second method uses a shapefile of an area of interest to subset a satellite image. Again the eau_claire_2011.img is used as the base image. A shapefile must be added in the Erdas image viewer containing the base image. For this exercise the ec_cpw_cts.shp shapefile was used. This area of interest incorporates Eau Claire and Chippewa Counties. When the shapefile is added to the viewer, it is overlaid on top of the base image (Figure 3).
To select the shapefile in the viewer as our AOI (area of interest) file, the shift key must be held while clicking on both counties. Once the shapefile has been selected, the HOME button is selected and then past from selected object is chosen. When this has been completed successfully, the AOI shapefile will be shown as dotted lines. The AOI shapefile must then be saved as an AOI file. This is accomplished by choosing SAVE AS, followed by AOI LAYER AS.
In order to complete this method of subsetting, the subset & chip tool is chosen under the raster tool set in Erdas. The input file and output location must be set prior to running the subset & chip tool. The AOI file is set by selected the AOI button on the bottom of the subset & chip tool window. After choosing the AOI button, the AOI file is input by navigating to its saved location and selected OK. For this process, all other parameters are left as the default values. Figure 4 displays the original satellite scene as well as the newly created subset image using the subset & chip tool.
 |
| Figure 1: Inquire Box used to subset a satellite image |
After the Inquire Box was set to cover the study area, the Subset & Chip tool within the raster toolset was clicked followed by the Create Subset Image tool which automatically populates the input image file to the current image in the viewer. To finish the process of subsetting with an Inquire Box, an output location must be set. The From Inquire Box parameter must be clicked to run this tool. This brings the coordinates of the image area covered by the inquire box into the subset interface. All other parameters within this tool interface may be left as default. Clicking OK creates a subset image. A model window will appear; once the model has successfully run, the window can be dismissed. A new subset image of the study area (Eau Claire, Wisconsin) has been created, but must be imported into the Erdas Viewer. Figure 2 displays the original eau_claire_2011.img as well as the newly created subset image.
 |
| Figure 2: Left Image-Satellite scene of Western, WI and Eastern, MN Right Image- Subset image of Eau Claire, WI |
Subset Using an Area of Interest Shapefile
The second method uses a shapefile of an area of interest to subset a satellite image. Again the eau_claire_2011.img is used as the base image. A shapefile must be added in the Erdas image viewer containing the base image. For this exercise the ec_cpw_cts.shp shapefile was used. This area of interest incorporates Eau Claire and Chippewa Counties. When the shapefile is added to the viewer, it is overlaid on top of the base image (Figure 3).
 |
| Figure 3: AOI Shapefile of Eau Claire and Chippewa Counties Overlaid on top of the eau_claire_2011.img base image |
In order to complete this method of subsetting, the subset & chip tool is chosen under the raster tool set in Erdas. The input file and output location must be set prior to running the subset & chip tool. The AOI file is set by selected the AOI button on the bottom of the subset & chip tool window. After choosing the AOI button, the AOI file is input by navigating to its saved location and selected OK. For this process, all other parameters are left as the default values. Figure 4 displays the original satellite scene as well as the newly created subset image using the subset & chip tool.
 |
| Figure 4: Subset using AOI file Right Image: Full satellite image scene Left Image: Subset image of Eau Claire and Chippewa Counties |
Image Fusion
Pan-sharpen is used to create a higher spatial resolution image from a coarse resolution image. This processes optimizes the image's spatial resolution for visual interpretation. For this exercise, a panchromatic image (ec_cpw_2000pan.img) with 15 meter resolution was used to pan-sharpen a reflective image (ec_cpw_2000.img) with 30 meter resolution of Eau Claire and Chippewa counties.
The pan-sharpen tool is located in the raster tool set in Erdas Imagine 2013. The Resolution Merge tool was used for this process. The panchromatic image was used as the high resolution input file within the resolution merge tool window and the reflective image was used as the multispectral input file. The multiplicative method was used in conjunction with the nearest neighbor resampling technique. Figure 5 displays the difference between the original image and the pan-sharpened image. The pan-sharpened image exhibits a higher degree of contrast than the original image. This allows for greater interpretation of colors in the pan-sharpened image.
Radiometric Enhancement Techniques
The pan-sharpen tool is located in the raster tool set in Erdas Imagine 2013. The Resolution Merge tool was used for this process. The panchromatic image was used as the high resolution input file within the resolution merge tool window and the reflective image was used as the multispectral input file. The multiplicative method was used in conjunction with the nearest neighbor resampling technique. Figure 5 displays the difference between the original image and the pan-sharpened image. The pan-sharpened image exhibits a higher degree of contrast than the original image. This allows for greater interpretation of colors in the pan-sharpened image.
 |
| Figure 5: Original image on the left, Pan-sharpened image on the right |
Haze Reduction
On radiometric enhancement technique is haze reduction. This process eliminates the appearance of haze in a remotely sensed satellite image. To conduct this process, the radiometric-haze reduction tool was used in the raster processing tools of Erdas Imagine 2013. All parameters of the haze reduction tool were used. The figure below displays the original image and the image produced by haze reduction (Figure 6). The haze that is visible in the original image has dramatically reduces in the newly created image using haze reduction. It should be noted that the areas that exhibited haze in the first image are not completely fixed in the second image; a shadow or transparent gray area is still present.
Linking View to Google Earth
On radiometric enhancement technique is haze reduction. This process eliminates the appearance of haze in a remotely sensed satellite image. To conduct this process, the radiometric-haze reduction tool was used in the raster processing tools of Erdas Imagine 2013. All parameters of the haze reduction tool were used. The figure below displays the original image and the image produced by haze reduction (Figure 6). The haze that is visible in the original image has dramatically reduces in the newly created image using haze reduction. It should be noted that the areas that exhibited haze in the first image are not completely fixed in the second image; a shadow or transparent gray area is still present.
 |
| Figure 6: Original image on the left, Image produced using the haze reduction tool on the right |
Google Earth can serve as an image interpretation key because it can show a true color aerial image of the features one is observing in the Erdas image viewer. It can help to better visualize almost all image interpretation keys such as texture, size, pattern, shadow, site and color. This is done by first adding a satellite image to the Erdas image viewer then clicking the Google Earth button on the top of the screen. Connect to Google Earth is selected next; this will open Google Earth. To link the Erdas image viewer with Google Earth, Match GE to View was selected. This sets the spatial extent of Google Earth to the image in Erdas. To synchronize Erdas and Google Earth, the Sync GE to View button is selected. Once the images are connected and synced, it is possible to use the zoom in and out buttons to view the image in Erdas and Google Earth at the same spatial extent. Figure 7 exhibits the desktop screen when the image in Erdas has been connected to Google Earth.
Resampling
Resampling is a mathematical technique used to create a new version of an image with a different pixel size. It is employed for image rectification or geometric collection purposes and when data is collected from different sensors with different pixel sizes. There are many methods of resampling. For this lab, the Nearest Neighbor and Bilinear Interpolation techniques were used.
 |
| Figure 7: Erdas & Google Earth image synchronization |
Resampling
Resampling is a mathematical technique used to create a new version of an image with a different pixel size. It is employed for image rectification or geometric collection purposes and when data is collected from different sensors with different pixel sizes. There are many methods of resampling. For this lab, the Nearest Neighbor and Bilinear Interpolation techniques were used.
Nearest Neighbor
The Nearest Neighbor technique was used to change the pixel size of an image from 30 meters by 30 meters to 30 meters by 20 meters. The Resample Pixel Size tool was used in the Spatial Raster tool set. Within the Resample Pixel Size tool, the Resample Method was left as the default because the default is the nearest neighbor method. The output cell size had to be set at 30 meters by 20 meters. All other parameters were left as the default values. Figure 8 displays the original image (30 m x 30 m) and the resampled image (30 m x 20 m).
The pixels in the resampled picture are rectangular shaped because the pixel size was resampled to 30 m by 20 m. Because the nearest neighbor technique was used, the pixel value is determined by the pixels adjacent to it. Therefore contrast is still apparent, but the areas of contrast are larger (Figure 9).
Bilinear Interpolation
The Nearest Neighbor technique was used to change the pixel size of an image from 30 meters by 30 meters to 30 meters by 20 meters. The Resample Pixel Size tool was used in the Spatial Raster tool set. Within the Resample Pixel Size tool, the Resample Method was left as the default because the default is the nearest neighbor method. The output cell size had to be set at 30 meters by 20 meters. All other parameters were left as the default values. Figure 8 displays the original image (30 m x 30 m) and the resampled image (30 m x 20 m).
 |
| Figure 8: Nearest Neighbor Resampling Technique |
 |
| Figure 9: Large scale view of original & resampled image |
Bilinear Interpolation
The same resampling process was followed to create a Bilinear Interpolation resampled image except the resampling method was changed to Bilinear Interpolation in the Resample Pixel Size tool. The pixels of the bilinear interpolated image smaller than the original image pixels because the image was resampled to a pixel size of 20 m by 20 meters. This causes there to be more contrast around borders of the features shown in the image (Figures 10 and 11).
 |
| Figure 10: Bilinear Method of resampling |
 |
| Figure 11: Large scale view of Bilinear Interpolation resampling technique |
Conclusion
This lab provided students with valuable skills in remote sensing. Study area delineation, synchronization to Google Earth, resampling, image subset and other radiometric enhancement techniques can be used to better interpret satellite imagery. This techniques are important to the field of remote sensing because the better interpretation of aerial imagery allows for better results of remote sensing analysis.
This lab provided students with valuable skills in remote sensing. Study area delineation, synchronization to Google Earth, resampling, image subset and other radiometric enhancement techniques can be used to better interpret satellite imagery. This techniques are important to the field of remote sensing because the better interpretation of aerial imagery allows for better results of remote sensing analysis.
No comments:
Post a Comment