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Spatially Adaptive Analysis and Segmentation of Polarimetric SAR Data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.ORCID iD: 0000-0002-3421-3835
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) has been one of the most important instruments for earth observation, and is increasingly used in various remote sensing applications. Statistical modelling and scattering analysis are two main ways for PolSAR data interpretation, and have been intensively investigated in the past two decades. Moreover, spatial analysis was applied in the analysis of PolSAR data and found to be beneficial to achieve more accurate interpretation results. This thesis focuses on extracting typical spatial information, i.e., edges and regions by exploring the statistical characteristics of PolSAR data. The existing spatial analysing methods are mainly based on the complex Wishart distribution, which well characterizes the inherent statistical features in homogeneous areas. However, the non-Gaussian models can give better representation of the PolSAR statistics, and therefore have the potential to improve the performance of spatial analysis, especially in heterogeneous areas. In addition, the traditional fixed-shape windows cannot accurately estimate the distribution parameter in some complicated areas, leading to the loss of the refined spatial details. Furthermore, many of the existing methods are not spatially adaptive so that the obtained results are promising in some areas whereas unsatisfactory in other areas. Therefore, this thesis is dedicated to extracting spatial information by applying the non-Gaussian statistical models and spatially adaptive strategies. The specific objectives of the thesis include: (1) to develop reliable edge detection method, (2) to develop spatially adaptive superpixel generation method, and (3) to investigate a new framework of region-based segmentation. Automatic edge detection plays a fundamental role in spatial analysis, whereas the performance of classical PolSAR edge detection methods is limited by the fixed-shape windows. Paper 1 investigates an enhanced edge detection method using the proposed directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and can overcome the limitation of fixed-shape windows by adaptively selecting homogeneous samples. The spherically invariant random vector (SIRV) product model is adopted to characterize the PolSAR data, and a span ratio is combined with the SIRV distance to highlight the dissimilarity measure. The experimental results demonstrated that the proposed method can detect not only the obvious edges, but also the tiny and inconspicuous edges in heterogeneous areas. Edge detection and region segmentation are two important aspects of spatial analysis. As to the region segmentation, paper 2 presents an adaptive PolSAR superpixel generation method based on the simple linear iterative clustering (SLIC) framework. In the k-means clustering procedure, multiple cues including polarimetric, spatial, and texture information are considered to measure the distance. Since the constant weighting factor which balances the spectral similarity and spatial proximity may cause over- or under-superpixel segmentation in different areas, the proposed method sets the factor adaptively based on the homogeneity analysis. Then, in heterogeneous areas, the spectral similarity is more significant than the spatial constraint, generating superpixels which better preserved local details and refined structures. Paper 3 investigates another PolSAR superpixel generation method, which is achieved from the global optimization aspect, using the entropy rate method. The distance between neighbouring pixels is calculated based on their corresponding DSDA regions. In addition, the SIRV distance and the Wishart distance are combined together. Therefore, the proposed method makes good use of the entropy rate framework, and also incorporates the merits of the SIRV distance and the Wishart distance. The superpixels are generated in a homogeneity-adaptive manner, resulting in smooth representation of the land covers in homogeneous areas, and well preserved details in heterogeneous areas.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. , p. 93
Series
TRITA-SOM, ISSN 1653-6126 ; 2017:14
Keywords [en]
Polarimetric SAR, Spatially adaptive, Edge detection, Spherically invariant random vector (SIRV), Superpixel, Simple linear iterative clustering (SLIC), Entropy rate, Segmentation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-218081ISBN: 978-91-7729-603-4 (print)OAI: oai:DiVA.org:kth-218081DiVA, id: diva2:1159514
Public defence
2017-12-12, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 09:30 (English)
Opponent
Supervisors
Note

QC 20171123

Available from: 2017-11-23 Created: 2017-11-22 Last updated: 2017-11-23Bibliographically approved
List of papers
1. Enhanced Edge Detection for Polarimetric SAR Images Using Directional Span-Driven Adaptive Window
Open this publication in new window or tab >>Enhanced Edge Detection for Polarimetric SAR Images Using Directional Span-Driven Adaptive Window
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Automatic edge detection for polarimetric synthetic aperture radar (PolSAR) images plays a fundamental role in various PolSAR applications. The classic methods apply the fixed-shape windows to detect the edges, whereas their performance is limited in heterogeneous areas. This paper presents an enhanced edge detection method for PolSAR data based on the directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and is constructed by adaptively selecting samples which follow the same statistical distribution. Therefore, it can overcome the limitation of classic fixed-shape windows. To obtain refined and reliable edge detection results in heterogeneous urban areas, we adopt the spherically invariant random vector (SIRV) product model, since the complex Wishart distribution is often not met. In addition, a span ratio is combined with the SIRV distance to highlight the dissimilarity measure and to improve the robustness of the proposed method. The simulated PolSAR data and three real data sets from ESAR, EMISAR and RADARSAT-2 systems are used to validate the performance of the enhanced edge detector. Both quantitative evaluation and visual presentation of the results demonstrate the effectiveness of the proposed method and its superiority over the classic edge detectors.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-218078 (URN)
Note

QC 20171123

Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2017-11-23Bibliographically approved
2. Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model
Open this publication in new window or tab >>Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model
2017 (English)In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 55, no 6, p. 3115-3131Article in journal (Refereed) Published
Abstract [en]

Simple linear iterative clustering (SLIC) algorithm was proposed for superpixel generation on optical images and showed promising performance. Several studies have been proposed to modify SLIC to make it applicable for polarimetric synthetic aperture radar (PolSAR) images, where the Wishart distance is adopted as the similarity measure. However, the superpixel segmentation results of these methods were not satisfactory in heterogeneous urban areas. Further, it is difficult to determine the tradeoff factor which controls the relative weight between polarimetric similarity and spatial proximity. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel generation method is proposed to overcome these limitations. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel generation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed superpixel generation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Spherically invariant random vector (SIRV), superpixel, edge detection, Simple linear iterative clustering (SLIC), polarimetric SAR (PolSAR).
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-187944 (URN)10.1109/TGRS.2017.2662010 (DOI)000402063500005 ()
Note

QC 20160607

Available from: 2016-06-01 Created: 2016-06-01 Last updated: 2017-11-22Bibliographically approved
3. Superpixel Segmentation of Polarimetric SAR Data Based on Integrated Distance Measure and Entropy Rate Method
Open this publication in new window or tab >>Superpixel Segmentation of Polarimetric SAR Data Based on Integrated Distance Measure and Entropy Rate Method
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2017 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 10, no 9, p. 4045-4058Article in journal (Refereed) Published
Abstract [en]

This paper proposes to integrate two different distances to measure the dissimilarity between neighboring pixels in PolSAR images, and introduces the entropy rate method into PolSAR image superpixel segmentation. Since the Gaussian model is commonly used for homogeneous scenes and less suitable for heterogeneous scenes, we adopt the spherically invariant random vector (SIRV) model to describe the back-scattering characteristics in heterogeneous areas. Moreover, a directional span-driven adaptive (DSDA) region is proposed such that it contains independent and identically distributed samples only, thus it can obtain accurate estimation of the distribution parameters. Using the DSDA region, the Wishart distance and SIRV distance are calculated, and then combined together through a homogeneity measurement. Therefore, the integrated distance takes advantage of the SIRV model and the Gaussian model, and suits both homogeneous and heterogeneous areas. Finally, based on the integrated distance, the superpixel segments are generated using the entropy rate framework. The experimental results on ESAR and PiSAR L-band datasets show that the proposed method can generate homogeneity-adaptive segments, resulting in smooth representation of the land covers in homogeneous areas, and better preserved details in heterogeneous areas.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Entropy rate, polarimetric SAR (PolSAR), segmentation, spherically invariant random vecor (SIRV), superpixel
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-218075 (URN)10.1109/JSTARS.2017.2708418 (DOI)000412626400021 ()2-s2.0-85020381501 (Scopus ID)
Note

QC 20171123

Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2017-11-23Bibliographically approved
4. Superpixel-Based Segmentation of Polarimetric SAR Images through Two-Stage Merging
Open this publication in new window or tab >>Superpixel-Based Segmentation of Polarimetric SAR Images through Two-Stage Merging
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper presents a superpixel-based segmentation method for multilook polarimetric SAR (PolSAR) data. By exploring the PolSAR statistics, a two-stage merging strategy is proposed to improve the segmentation efficiency and accuracy. First, based on the initial superpixel partition, the Wishart-merging stage (WMS) simultaneously merges the regions in homogeneous areas, which can be modelled by the Wishart distribution. The Wishart energy loss together with the edge penalty is utilized so that the merged superpixels are from the same land cover and without ambiguity. In the second stage, the doubly flexible two-parameter KummerU distribution is applied to better characterize the resultant regions from the WMS, which are usually located in heterogeneous areas. This KummerU-merging stage (KUMS) iteratively merges the adjacent regions based on the KummerU energy loss. In addition, the edge penalty and the proposed homogeneity penalty are also adopted to guide the merging procedure, and prevent merging the regions from distinct land covers. Compared with the classical iterative merging methods, the two-stage merging strategy can improve the efficiency based on the WMS, and increase the segmentation accuracy through the KUMS. Two experimental PolSAR datasets acquired by ESAR and EMISAR system are used to demonstrate the effectiveness of the proposed segmentation method.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-218079 (URN)
Note

QC 20171123

Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2017-11-23Bibliographically approved

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