Lab 10: Radar Remote Sensing

Introduction

The purpose of this lab is to introduce students to radar remote sensing. This lab specifically aims to increase knowledge of noise reduction in radar images, spectral and spatial enhancement, multi-sensor fusion, texture analyse, polarimetric process and slant-range conversion. 

Methods

The first step of this lab was to reduce speckling of radar images. This was done in Erdas Imagine. For this section we used the Radar Speckle Suppression tool. This is first done by calculating the coeffecient of variation, which for this lab was 0.274552. This value was used to run the Radar Speckle Suppression.  This tool was run in 3 iterations with each iteration using different filters (fig. 1).  After the different iterations were run, the histograms of each of the outputs was examined (fig. 2).

Figure 1. The different parameters used to run the different iterations of the Radar Speckle Reduction tool.

Figure 2. Each of the histograms from the different iterations. The image on the left is the original image. As the more iterations were run the image became smoother.

After despeckling the image, the next process that needed to be run was the edge enhancement tool. This tool enhances the ability for an analyst to better delineate different surface features in an image (fig. 3) The Wallis Adaptive Filter tool was also run as part of image enhancement.

Following image enhancement, we were asked to perform a sensor merge. A sensor merge combines both radar imagery and landsat imagery to create a single image that has characteristics of sensors. This was done by using IHS principle component sensor merge. This replaces the RGB values of the landsat image with the greys-scale values from the radar image. This produces a composite image from both sensors (fig. 4).

Following the image merge we were asked to run a texture analysis on radar imagery using an image from Flevoland, Holland using the C-band with a 20 meter spatial resolution. The texture analyse created an image that allows for areas of similar texture characteristics to be visualized more easily (fig . 5).

Polarimetric SAR Processing was also performed. The image for the section of the lab was taken over the northern section of Death Valley. This was completed by using band synthesis using 4 different polarization combinations. There were also four different stretch methods applied to the imagery, Gaussian, Linear, and Square root. Of the three mentioned methods the Gaussian method produced the best results (fig. 6 - 8).

The final section of the lab consisted of Slant-to-Ground Range Transformation. This reduces the geometric distortion of radar images from the slanted angle that the radar images are captured at (fig. 9).

Results

Figure 3. Edge Enhanced image (right) allowing for ridged features to be more discernible.

Figure 4. The merged (left) that incorporates characaristics from both radar and Landsat imagery (left).

Figure 5. Texture Analysis output.

Figure 6. Gaussian histogram stretched image and histogram

Figure 7. Linear stretched image and histogram

Figure 8. Square Root Stretched Image

Figure 9. Slant-to-Ground Range Transformation  corrected image on the right. The corrected image has less geometric distortion than the original image on the left.