GEOG 438 Advanced Remote Sensing: May 2019

Goal

The goal of this lab exercise was to introduce the class to the basics of working with remotely sensed radar images including preprocessing and processing of said images. This was accomplished by performing noise reduction through speckle filtering, both spatial and spectral enhancement, multi-sensor fusion, texture analysis, polarimetric processing, and finally slant-range to ground conversion.

Methods

Part 1 - Speckle Reduction and Edge Enhancement

In this section of the lab, we used the provided radar image and the Radar Speckle Suppression tool to conduct despeckling. Using the calculated Coefficient of Variation as well as two other provided Coefficients of Variation, we ran the Radar Speckle Suppression tool three times, also changing the Coefficient of Variation Multiplier and the Window Size parameters each time the tool was run. Once all the despeckled images were created, we then viewed the histograms for each to compare them. Next, we ran the Non-Directional edge tool on the original provided image and our third despeckled image using the Prewitt Output Options parameter for each. Next, we used the Radar Speckle Suppression tool with a different provided radar image and set the filter to Gamma-Map and then used the Adaptive Filter tool to set the data type to Stretch to Unsigned 8-Bit, the Moving Window to 3, and the Multiplier to 3 as well.

Part 2 - Sensor Merge, Texture Analysis, and Brightness Adjustment

The next portion of this lab started with using the Sensor Merge tool and merging a multispectral and a gray scale image using the IHS method, Nearest Neighbor resampling, IHS Substitution, and setting the data type to Stretch to Unsigned 8-Bit. Once this was done, the next portion of the lab was about using the Texture Analysis tool with a Window Size of 5 and a Skewness Operator. Once this was done, the next tool to run was the Brightness Adjustment tool with the Output Options set to Column and the Data Type set to Float Single.

Part 3 - Polarimetric SAR Processing and Analysis

For part 3 of this lab, ENVI was used and the Synthesize SIR-C Data tool was used and run multiple times with different parameters. These parameters included changing the Transmit and Receive Ellip as well as the Transmit and Receive Orien. These different parameters produced different polarization images. Once this was done, the images were displayed in either Gaussian, Linear, or Square-Root stretches. Next, we restored ROI's for the image and then used those ROI's and the Extract Polarization Signatures > SIR-C tool to create Polarization Signature Viewer windows.

Part 4 - Slant-to-Ground Range Transformation

For the final portion of this lab, we first previewed the CEOS Header of an image by clicking on the View Generic CEOS Header button and opening up the provided image. Next, we resampled the image using the Slant to Ground Range > SIR-C tool and set the Output Pixel Size (m) to 13.32 and the Resampling Method to Bilinear and then ran the tool.