NDLI: Filtering effects on polarimetric SAR image classification

Content Provider	IEEE Xplore Digital Library
Author	Chen, K.S. Tzeng, Y.C. Chen, C.T. Lee, J.S.
Copyright Year	1997
Description	Author affiliation: Centre for Remote Sensing Res., Nat. Central Univ., Chung-Li, Taiwan (Chen, K.S.)
Abstract	Feature extraction from SAR images is usually impeded by the presence of speckle noise. This becomes more serious in the case of polarimetric SAR system. A polarimetric filter recently proposed by Lee et al. [1997] emphasizes not introducing additional cross-talk and statistical correlation between channels, preserving polarimetric information and not degrading the image quality. This paper exams its effects on the image classification by a supervised fuzzy dynamic learning neural network trained by a Kalman filter technique. Based on the available ground truth, the classification performance were evaluated using the original and filtered SAR images. Two independent test sites are selected for this purpose. The first case is a P-band JPL polarimetric SAR data over Les Landes for tree age classification. A total of 12 classes between 5 to 44 years of age were to be classified, along with a bare soil type. The second test site is over Flevoland of the Netherlands. This agricultural site consists of 11 landcover types. Again, the polarimetric SAR data were acquired with JPL P, L, C bands airsar system. For the first case, it was found that the overall classification accuracy was able to improve from 69% to about 86% with kappa coefficient up from 0.46 to 0.76. Substantial improvement was also confirmed for the second case. In particular, when classification was performed using only single frequency. This shows that the polarimetric information are well preserved. By visual inspection from classified map, the land cover boundaries were also delineated more clearly. As for fuzzy neural network performance, among the tested cases, the fuzzy index equal to 2 gets the best results.
Starting Page	1199
Ending Page	1201
File Size	303692
Page Count	3
File Format	PDF
ISBN	0780338367
DOI	10.1109/IGARSS.1997.606396
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-08-03
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Image classification Fuzzy neural networks Testing Feature extraction Impedance Speckle Crosstalk Filters Degradation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Polarimetric SAR speckle filtering and its impact on classification

Influence of speckle filtering of Polarimetric SAR data on different classification methods

Polarimetric SAR speckle filtering and its implication for classification

Classification Of SAR-data Using Neural Networks And Polarimetric Filters

Polarimetric signature preservation in SAR speckle filtering

Speckle filtering algorithm for polarimetric SAR based on mean shift

Street tracking based on SAR data from urban areas

Texture Classification using Convolutional Neural Networks

Speckle Reduction of Polarimetric SAR Image Based on ICA-SCS Algorithm

Filtering effects on polarimetric SAR image classification

Similar Documents

Polarimetric SAR speckle filtering and its impact on classification

Influence of speckle filtering of Polarimetric SAR data on different classification methods

Polarimetric SAR speckle filtering and its implication for classification

Classification Of SAR-data Using Neural Networks And Polarimetric Filters

Polarimetric signature preservation in SAR speckle filtering

Speckle filtering algorithm for polarimetric SAR based on mean shift

Street tracking based on SAR data from urban areas

Texture Classification using Convolutional Neural Networks

Speckle Reduction of Polarimetric SAR Image Based on ICA-SCS Algorithm

Filtering effects on polarimetric SAR image classification