TSPol-ASLIC: Adaptive Superpixel Generation With Local Iterative Clustering for Time-Series Quad- and Dual-Polarization SAR Data

The superpixel generation is a key step for object-based classification and change detection. For the time-series polarimetric synthetic aperture radar (PolSAR) superpixel generation, the traditional polarimetric similarity measure based on the joint covariance matrix has limitations in discriminati...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 15
Hlavní autoři:	Gao, Han, Wang, Changcheng, Xiang, Deliang, Ye, Jiawei, Wang, Guanya
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Clustering Clustering algorithms Covariance matrices Covariance matrix Detection Dual polarization radar Edge detection Fluctuations Geologic measurements Homogeneity Image segmentation Methods Polarimetric synthetic aperture radar (PolSAR) Polarimetry Polarization Radar Radar imaging Radarsat SAR (radar) Satellites Seeds Sequences Similarity Similarity measures simple local iterative clustering (SLIC) superpixel generation Synthetic aperture radar Time series time-series polarimetric SAR (TSPol)-adaptive simple local iterative clustering (ASLIC)
ISSN:	0196-2892, 1558-0644
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Abstract	The superpixel generation is a key step for object-based classification and change detection. For the time-series polarimetric synthetic aperture radar (PolSAR) superpixel generation, the traditional polarimetric similarity measure based on the joint covariance matrix has limitations in discriminating different time-series similarity sequences with different fluctuations. Besides, in the traditional time-series PolSAR superpixel generation methods, it is difficult to determine the tradeoff factor between polarimetric and spatial similarity. In this article, an adaptive time-series PolSAR superpixel generation method based on the simple local iterative clustering (SLIC) is proposed, named time-series polarimetric SAR (TSPol)-adaptive simple local iterative clustering (ASLIC). There are three main improvements. First, a novel time-series polarimetric similarity measure based on the root mean square (rms) is proposed. Multitemporal polarimetric statistical information is combined to describe the polarimetric proximity between pixels, referring to the rms of the multitemporal proximities. Second, an edge detection method based on the stacked 2-D Gaussian-shaped (s2-D GS) window is proposed to initialize the central seeds for superpixel generation. Third, an improved SLIC clustering similarity combined with the time-series polarimetric, time-series power, and spatial similarities is proposed. Meanwhile, a homogeneity factor is applied to adaptively balance the relative weights of various similarities. We use eight Radarsat-2 quad-polarization synthetic aperture radar (SAR) images and 14 Sentinel-1 dual-polarization SAR images to evaluate the effectiveness. The results show our similarity measure and superpixel generation results are superior to those of the traditional methods. For example, as for the Radarsat-2 data, the improvement of the boundary recall by the proposed similarity measure and homogeneity factor is about 4% and 10%, respectively.
AbstractList	The superpixel generation is a key step for object-based classification and change detection. For the time-series polarimetric synthetic aperture radar (PolSAR) superpixel generation, the traditional polarimetric similarity measure based on the joint covariance matrix has limitations in discriminating different time-series similarity sequences with different fluctuations. Besides, in the traditional time-series PolSAR superpixel generation methods, it is difficult to determine the tradeoff factor between polarimetric and spatial similarity. In this article, an adaptive time-series PolSAR superpixel generation method based on the simple local iterative clustering (SLIC) is proposed, named time-series polarimetric SAR (TSPol)-adaptive simple local iterative clustering (ASLIC). There are three main improvements. First, a novel time-series polarimetric similarity measure based on the root mean square (rms) is proposed. Multitemporal polarimetric statistical information is combined to describe the polarimetric proximity between pixels, referring to the rms of the multitemporal proximities. Second, an edge detection method based on the stacked 2-D Gaussian-shaped (s2-D GS) window is proposed to initialize the central seeds for superpixel generation. Third, an improved SLIC clustering similarity combined with the time-series polarimetric, time-series power, and spatial similarities is proposed. Meanwhile, a homogeneity factor is applied to adaptively balance the relative weights of various similarities. We use eight Radarsat-2 quad-polarization synthetic aperture radar (SAR) images and 14 Sentinel-1 dual-polarization SAR images to evaluate the effectiveness. The results show our similarity measure and superpixel generation results are superior to those of the traditional methods. For example, as for the Radarsat-2 data, the improvement of the boundary recall by the proposed similarity measure and homogeneity factor is about 4% and 10%, respectively.
Author	Gao, Han Wang, Guanya Ye, Jiawei Xiang, Deliang Wang, Changcheng
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SubjectTerms	Clustering Clustering algorithms Covariance matrices Covariance matrix Detection Dual polarization radar Edge detection Fluctuations Geologic measurements Homogeneity Image segmentation Methods Polarimetric synthetic aperture radar (PolSAR) Polarimetry Polarization Radar Radar imaging Radarsat SAR (radar) Satellites Seeds Sequences Similarity Similarity measures simple local iterative clustering (SLIC) superpixel generation Synthetic aperture radar Time series time-series polarimetric SAR (TSPol)-adaptive simple local iterative clustering (ASLIC)
Title	TSPol-ASLIC: Adaptive Superpixel Generation With Local Iterative Clustering for Time-Series Quad- and Dual-Polarization SAR Data
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