A Tutorial and Performance Analysis on ENVI Tools for SAR Image Despeckling

Author(s): Mohammad R. Khosravi*, Babak Bahri-Aliabadi, Seyed R. Salari, Sadegh Samadi, Habib Rostami, Vahid Karimi

Journal Name: Current Signal Transduction Therapy

Volume 15 , Issue 2 , 2020

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Abstract:

Background: The presence of speckle noise in synthetic aperture radar (SAR) images makes the images of low quality in terms of textural features and spatial resolution which are required for processing issues such as image classification and clustering. Already, there are many adaptive filters to remove noise in SAR images. ENVI software is a fully applicable tool for this purpose which has a good library including several filters in the classes of adaptive, orderstatistics and non-linear filters.

Materials and Methods: In this study, the toolbox of ENVI is reviewed, analyzed and then numerically evaluated based on several single-band images along with multi-band polarimetric SAR (Pol-SAR) images achieved from SAR sensors such as TerraSAR-X. For evaluation, two metrics including Equivalent Number of Looks (ENL) and Edge Preservation Index (EPI) are used which show the ability of the filters in preserving jointly spatial/textural features based on general information and edges quality, respectively.

Results: It is notable that both metrics illustrate that some classic filters are better in comparison to newer filters.

Conclusion: The experiments can help us in selecting a better filter towards our aims. In this respect, attention to the results of commercial filters of ENVI software and their analysis can guide us to find the best case in order to process commercial data of SAR sensors in the applications of environmental monitoring, geo-science studies, industrial usages and so on.

Keywords: Synthetic Aperture Radar (SAR), speckle noise, denoising, ENVI, radio imaging, remote sensing of environment.

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Article Details

VOLUME: 15
ISSUE: 2
Year: 2020
Published on: 01 December, 2020
Page: [215 - 222]
Pages: 8
DOI: 10.2174/1574362413666181005101315

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