Lab 10: Radar

Goal:

The goal of this lab is to get a introduction and to gain a small amount of experience working with radar data and imagery.  In this lab a few different processes will be done to radar data, noise reduction using speckle filtering, spectral and spatial enhancement, multi-sensor fusion, texture analysis, polarimetric processing, and slant-range to ground range conversion.



Methods:

The first technique used was speckle filtering.  Speckle filtering takes a radar image that looks grainy and cleans it up.  This is done using a tool in ERDAS Imagine. The radar image used is from the Shuttle Imaging Radar (SIR-A).  The scene is of the Lop Nor Lake in the XinJiang Province in China.  The Radar Speckle Suppression tool was used in ERDAS and multiple parameters were set.   The coefficient of variation, coefficient of variation multiplier, and the window size were the parameters that were changed when running 3 different speckle filters.  The following image shows what images were output and what the parameters were on those images. The despeckled images were ran through the process multiple times.  The results from these filters can be seen in the results section.

Parameters

There are multiple radar image enhancement tools available within ERDAS, these are Wallis adaptive filter, Sensor Merge, Texture Analysis, and Brightness Adjustment.

Wallis adaptive filter is designed to adjust the contrast stretch of an image using only the values within a local region, which makes it widely applicable.  The image input into the Adaptive filter tool is despeckle 4, which is just a despeckled image, the window size is set to 3 and the multiplier is set to 3 as well, these are the only parameters changes in the tool.  The resulting image can be seen labeled "Enhanced" in the results section of the lab. The following image is the input image.

Despeckle 4

Texture analysis is another tool that can alter the sensitivity of texture within a radar image. The texture analysis tool in ERDAS is used to accomplish this.  The only parameter changed within the tool is the window size, which is set to 5.  The resulting image can be seen in the results section labeled "Texture".  This tool will highlight the areas of the image that contain large amounts of texture.  The following image is the input image.

Texture and Brightness Input

The next tool used is a brightness filter.  This works by adjusting the pixel brightness values so that each line of constant range has the same average.  The input image for this is the same image that was input for the texture analysis.  The resulting image will be labeled "Brightness" in the results section.



Results:

The despeckled images appear to be less grainy the more times they are ran through the filter.  Despeckle 1 looks to have fine grains of speckling and by the time is has been ran through the tool a few more times the speckles appear to be much large and combined together.  Running it through the tool does not necessarily make it a better more clear image,  the image can actually become distorted the more times it is ran through the speckle filter.

Despeckle 1

Despeckle 2

Despeckle 3

The following image is the image enhanced using the Wallis Adaptive Filter.  It appears to be more crisp and clean than the input image despeckle 4.

Enhanced

The texture analysis tool highlighted all the areas that have lots of texture.  It is hard to tell what anything is when just looking at the resulting image but pairing that with the input helps to make sense of what the high texture areas are. 

Texture

The brightness tool appears to have lightened up many of the darker areas of the image.  When comparing this to the input image it is evident when looking at the upper left corner of the image that many of the pixels appear to be brighter.

Brightness

Sources:

Cyril Wison