Polarimetric synthetic aperture radar image processing for land cover classification

This thesis presents the processing design and development towards an improved land cover classification of multi-look POLSAR data. In this context, three related processing aspects, namely edge detection, speckle suppression and region-based seg¬mentation, were investigated. To detect edges in spec...

Full description

Saved in:
Bibliographic Details
Main Author: Lee, Ken Yoong
Other Authors: Timo Rolf Bretschneider
Format: Theses and Dissertations
Language:English
Published: 2009
Subjects:
Online Access:https://hdl.handle.net/10356/14956
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-14956
record_format dspace
spelling sg-ntu-dr.10356-149562023-03-04T00:39:54Z Polarimetric synthetic aperture radar image processing for land cover classification Lee, Ken Yoong Timo Rolf Bretschneider Lau Chiew Tong School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision This thesis presents the processing design and development towards an improved land cover classification of multi-look POLSAR data. In this context, three related processing aspects, namely edge detection, speckle suppression and region-based seg¬mentation, were investigated. To detect edges in speckle-corrupted POLSAR data, two new edge detectors, which are based separately on the Roy’s largest eigenvalue and trace ratio, were proposed. Their capabilities were compared with three other edge detectors, which use the likelihood ratio, Dowson-Landau metric and Euclidean distance, respectively. Applied to nine-look NASA/JPL POLSAR C- and L-band data, both the proposed edge detectors exhibited their satisfactory performance in detecting edges while coping with speckle noise disturbance. For the speckle suppression, a novel spatially adaptive speckle filter was developed, which aims at preserving image features during filtering process. In the experiments, the proposed filter showed its good performance in speckle removal, image feature retention and radiometric preservation. Comparisons with the boxcar, Lee-refined and annealing filters were carried out. In the area of region-based segmentation, an existing hybrid segmen¬tation algorithm was extended for multi-look POLSAR data. The capabilities of the extended algorithm were examined and bench¬marked against the HSWO algorithm. From the results, it was noticed that the segmentation outputs from both the algorithms relied strongly on the user-defined region number, which is employed as the termination rule. Finally, the classification of NASA/JPL POLSAR data over a selected area of Kuala Muda in Peninsular Malaysia was carried out based separately on scattering mechanisms and statistical distribution. For the scattering property-based unsupervised classification, three different schemes were examined, namely van Zyl’s scattering classification, Freeman-Durden scattering model and Cloude-Pottier target decomposition. The derived scattering mechanisms of different land cover classes were studied. In the supervised complex Wishart classification, the obtained overall accuracies were 63% and 67% for both the C- and L-band, respectively. An improved accuracy of 75% was attained by using the dual-frequency input. Furthermore, comparison between per-pixel and per-segment classification approaches was conducted, where the per-segment approach was expectedly found to improve the classification accuracy. DOCTOR OF PHILOSOPHY (SCE) 2009-03-11T00:50:11Z 2009-03-11T00:50:11Z 2009 2009 Thesis Lee, K. Y. (2009). Polarimetric synthetic aperture radar image processing for land cover classification. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/14956 10.32657/10356/14956 en 202 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Lee, Ken Yoong
Polarimetric synthetic aperture radar image processing for land cover classification
description This thesis presents the processing design and development towards an improved land cover classification of multi-look POLSAR data. In this context, three related processing aspects, namely edge detection, speckle suppression and region-based seg¬mentation, were investigated. To detect edges in speckle-corrupted POLSAR data, two new edge detectors, which are based separately on the Roy’s largest eigenvalue and trace ratio, were proposed. Their capabilities were compared with three other edge detectors, which use the likelihood ratio, Dowson-Landau metric and Euclidean distance, respectively. Applied to nine-look NASA/JPL POLSAR C- and L-band data, both the proposed edge detectors exhibited their satisfactory performance in detecting edges while coping with speckle noise disturbance. For the speckle suppression, a novel spatially adaptive speckle filter was developed, which aims at preserving image features during filtering process. In the experiments, the proposed filter showed its good performance in speckle removal, image feature retention and radiometric preservation. Comparisons with the boxcar, Lee-refined and annealing filters were carried out. In the area of region-based segmentation, an existing hybrid segmen¬tation algorithm was extended for multi-look POLSAR data. The capabilities of the extended algorithm were examined and bench¬marked against the HSWO algorithm. From the results, it was noticed that the segmentation outputs from both the algorithms relied strongly on the user-defined region number, which is employed as the termination rule. Finally, the classification of NASA/JPL POLSAR data over a selected area of Kuala Muda in Peninsular Malaysia was carried out based separately on scattering mechanisms and statistical distribution. For the scattering property-based unsupervised classification, three different schemes were examined, namely van Zyl’s scattering classification, Freeman-Durden scattering model and Cloude-Pottier target decomposition. The derived scattering mechanisms of different land cover classes were studied. In the supervised complex Wishart classification, the obtained overall accuracies were 63% and 67% for both the C- and L-band, respectively. An improved accuracy of 75% was attained by using the dual-frequency input. Furthermore, comparison between per-pixel and per-segment classification approaches was conducted, where the per-segment approach was expectedly found to improve the classification accuracy.
author2 Timo Rolf Bretschneider
author_facet Timo Rolf Bretschneider
Lee, Ken Yoong
format Theses and Dissertations
author Lee, Ken Yoong
author_sort Lee, Ken Yoong
title Polarimetric synthetic aperture radar image processing for land cover classification
title_short Polarimetric synthetic aperture radar image processing for land cover classification
title_full Polarimetric synthetic aperture radar image processing for land cover classification
title_fullStr Polarimetric synthetic aperture radar image processing for land cover classification
title_full_unstemmed Polarimetric synthetic aperture radar image processing for land cover classification
title_sort polarimetric synthetic aperture radar image processing for land cover classification
publishDate 2009
url https://hdl.handle.net/10356/14956
_version_ 1759855465645211648