Image Mosaic & Miscellaneous Image Functions II
Goal
Remotely sensed data calls for different analytic processes than other fields of GIS. The following processes were introduced through a lab in Geography 338 at the University of Wisconsin-Eau Claire. The lab included RGB to IHS and IHS to RGB transformations, spatial and spectral image enhancement, band ratio as well as binary change detection. Image mosaic was also performed in the lab.
Methods
RGB IHS Transformation
These transformations provide an alternative way of displaying RGB (red, green, blue) primary additive colors. This processes changes red, green and blue to intensity, hue, saturation. Because often RGB colors often lack saturation, this transformation is used to improve interpretation of multispectral color composites of images. The figure below shows the differences between the original image and the newly created IHS image.
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| Figure 1: RGB to IHS Transformation Left: Original Image Right: IHS Image |
The IHS image is not what would be seen in the natural world. The image exhibits more contrast than the original image due to the increased orange and green tones. When zoomed in, it is much harder to differentiate between features on the original image compared to the IHS image.
An IHS image can be subsequently transformed back to RGB to display the colors closely as they are perceived by the human eye. When transforming back to RGB, band one represents intensity, band 2 represents hue and band 3 represents saturation as opposed to transforming to IHS when band 1 represents blue, band 2 represents green, and band 3 represents red. Transforming from RGB to IHS using a stretched method is the best way to display earth's features as they would appear in nature (Figure 2). The stretched image also has much better resolution than the other two images.
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| Figure 2: RGB to IHS transformed image using a stretch method |
Image Mosaicking
Mosaicking is used when an area of interest is larger than the extent of one satellite image scene or the area of interest intersects two adjacent satellite image scenes. Two methods of mosaicking were introduced in this lab. The first method used was Mosaic Express. In this tool interface, it is important that the input images are added so that the best quality image will be laid over the lesser quality image. For this lab, all default parameters were kept.
The second method of mosaicking is MosaicPro. The images were brought in the same way as the previous method, but this time, before the images were added to the viewer Compute Active Area was selected in the Image Area Options. All parameters were accepted because it was not necessary to crop or reduce the spatial extent of the output image. the appearance of the viewer is shown in Figure 4 when the images have been added.
In the MosaicPro viewer, images can be selected and sent to the bottom or top. This tool is useful so the best quality image is placed on the top. The Color Correction-Histogram Matching tool was used to synchronize the radiometric properties of both images before the mosaic was performed. Figure 5 displays the mosaicked image using MoscaicPro.
Mosiac Express is only recommended for visual interpretation of images, not for analysis of remotely sensed images. The transition between the mosaicked images is not as smooth as the original images appear in the viewer. The bottom image exhibits much more red coloring than the image on top. The next method of mosaicking produces better results because the images the radiometric properties of both images are more synchronized. (Figure 6).The image created by the Mosaic Pro has a much smoother transition at the overlap area of both images.
Band Ratioing
In this lab, band ratioing was performed by implementing normalized difference vegetation index (NDVI). This process was performed by first adding the image to the ERDAS viewer and then activating the Raster-Unsupervised tool. In the tool interface, the sensor was set to Landsat TM and the function was set to NDVI. The image below displays the original image and the output image created by this tool (Figure 7).
In the NDVI image, the areas that are medium gray or black most likely do not have high concentrations of vegetation. The areas of dark black are water and therefore the vegetation that exists is covered by a large quantity of water, so the sensor would not pick up the vegetation.
Spatial Enhancement
5 X 5 low pass convolution filtering
This tool is used to suppress images with high frequency. High frequency refers to significant changes in brightness values over short distances in remotely sensed images. This method is a spatial enhancement technique that creates more contrast in the output image.
5 X 5 high pass convolution filtering
When an images has low frequency (few changes in brightness values over a given area), a 5 X 5 high pass convolution filter can be performed to improve brightness values. The newly created image is much darker in color, but there is more contrast and the resolution is much better (Figure 9).
Spectral Enhancement
Minimum-Maximum Linear Contrast Stretch
This type of linear stretch is applied to Gaussian histograms to spread the range of brightness values for more contrast in the resulting image.
Piecewise Linear Contrast Stretch
This type of spectral enhancement is applied when an image's histogram has more than one mode. For this lab the image had three modes, so it was considered trimodal. Piecewise contrast stretch redistributes pixel values of the original image. The resulting image's pixels values are more equally distributed in value.
Histogram Equalization
Histogram equalization is performed to improve contrast of an image for better visual interpretation. The process calls for the use of the Raster-Radiometric-Histogram Equalization tool in ERDAS Imagine 2013. For this lab, all defaults were accepted in the tools interface.
The newly created image (Figure 12) has many more areas of white/light grays than the original image. There is a drastic change in the image’s histogram as well, the new image histogram is starched from 39 to 256 as opposed to 14 to 44 (approximately). This means the new image has much more contrast than the original image.
Binary Change Detection
Binary change detection is used estimate and map brightness values of pixels that have changed from one specified time to another. For this lab, the area of interest was Eau Claire County, Wisconsin between August 1991 and August 2011.
The change of brightness values was analyzed spectrally using the image differencing technique. In ERDAS, the Two Input Operators tool was used for the change detection process for layer 4 of the images. The resulting image does not show areas of change. The threshold of change must be determined before the areas of change can be visually interpreted. The equation (mean +1.5 standard deviation) is used to calculate the threshold. The threshold is added to the center value of the histogram for the upper threshold and subtracted from the center value of the histogram for the lower threshold of change (Figure 13).
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| Figure 3: Mosaic Express Output Image |
The second method of mosaicking is MosaicPro. The images were brought in the same way as the previous method, but this time, before the images were added to the viewer Compute Active Area was selected in the Image Area Options. All parameters were accepted because it was not necessary to crop or reduce the spatial extent of the output image. the appearance of the viewer is shown in Figure 4 when the images have been added.
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| Figure 4: MosaicPro viewer with images to be mosaicked |
In the MosaicPro viewer, images can be selected and sent to the bottom or top. This tool is useful so the best quality image is placed on the top. The Color Correction-Histogram Matching tool was used to synchronize the radiometric properties of both images before the mosaic was performed. Figure 5 displays the mosaicked image using MoscaicPro.
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| Figure 5: Mosaicked image using Mosaic Pro |
Mosiac Express is only recommended for visual interpretation of images, not for analysis of remotely sensed images. The transition between the mosaicked images is not as smooth as the original images appear in the viewer. The bottom image exhibits much more red coloring than the image on top. The next method of mosaicking produces better results because the images the radiometric properties of both images are more synchronized. (Figure 6).The image created by the Mosaic Pro has a much smoother transition at the overlap area of both images.
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| Figure 6: MosaicPro Output Image (left) Mosaic Express Output Image (right) |
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| Figure 7: Original Image (left) NDVI Output Image (right) |
In the NDVI image, the areas that are medium gray or black most likely do not have high concentrations of vegetation. The areas of dark black are water and therefore the vegetation that exists is covered by a large quantity of water, so the sensor would not pick up the vegetation.
Spatial Enhancement
5 X 5 low pass convolution filtering
This tool is used to suppress images with high frequency. High frequency refers to significant changes in brightness values over short distances in remotely sensed images. This method is a spatial enhancement technique that creates more contrast in the output image.
 |
| Figure 8: High Frequency Image (left) 5 x 5 low pass convolution image (right) |
When an images has low frequency (few changes in brightness values over a given area), a 5 X 5 high pass convolution filter can be performed to improve brightness values. The newly created image is much darker in color, but there is more contrast and the resolution is much better (Figure 9).
 |
| Figure 9: Low Frequency Image (left) 5 x 5 high pass convolution image (right) |
Spectral Enhancement
Minimum-Maximum Linear Contrast Stretch
This type of linear stretch is applied to Gaussian histograms to spread the range of brightness values for more contrast in the resulting image.
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| Figure 10: Resulting image of minimum-maximum contrast stretch |
This type of spectral enhancement is applied when an image's histogram has more than one mode. For this lab the image had three modes, so it was considered trimodal. Piecewise contrast stretch redistributes pixel values of the original image. The resulting image's pixels values are more equally distributed in value.
 |
| Figure 11: Resulting image of piecewise contrast stretch |
Histogram equalization is performed to improve contrast of an image for better visual interpretation. The process calls for the use of the Raster-Radiometric-Histogram Equalization tool in ERDAS Imagine 2013. For this lab, all defaults were accepted in the tools interface.
The newly created image (Figure 12) has many more areas of white/light grays than the original image. There is a drastic change in the image’s histogram as well, the new image histogram is starched from 39 to 256 as opposed to 14 to 44 (approximately). This means the new image has much more contrast than the original image.
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| Figure 12: Histogram Equalization |
Binary Change Detection
Binary change detection is used estimate and map brightness values of pixels that have changed from one specified time to another. For this lab, the area of interest was Eau Claire County, Wisconsin between August 1991 and August 2011.
The change of brightness values was analyzed spectrally using the image differencing technique. In ERDAS, the Two Input Operators tool was used for the change detection process for layer 4 of the images. The resulting image does not show areas of change. The threshold of change must be determined before the areas of change can be visually interpreted. The equation (mean +1.5 standard deviation) is used to calculate the threshold. The threshold is added to the center value of the histogram for the upper threshold and subtracted from the center value of the histogram for the lower threshold of change (Figure 13).
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| Figure 13: Threshold of change |
Model Maker was used to map the areas of change for the area of interest using the conditional function EITHER 1IF ($n1_ec_91>change/nochangethreshold value)OR 0 OTHERWISE. This function will show all pixels with values above the change/no change threshold value and will makes all pixels with values below the threshold. The resulting image was brought into ESRI Desktop 10.1 ArcMap for better interpretation of where the changes occurred (Figure 14). There are more areas that didn’t change than did change between 1991 and 2011. Areas that changed are mostly located near urban centers or large water bodies like lakes or rivers.
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| Figure 14: Binary change detection Eau Claire, Wisconsin and surrounding areas |
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