XXII ISPRS Congress 2012: Technical Commission VII

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/7: THEORY AND EXPERIMENTS IN RADAR AND LIDAR]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: AUTOMATIC EXTRACTION OF WATER IN HIGH-RESOLUTION SAR IMAGES BASED ON MULTI-SCALE LEVEL SET METHOD AND OTSU ALGORITHM H. G. Sui, C. Xu

Document type:: Multivolume work

Structure type:: Chapter

ronics and 23905), the ndamental International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia AUTOMATIC EXTRACTION OF WATER IN HIGH-RESOLUTION SAR IMAGES BASED ON MULTI-SCALE LEVEL SET METHOD AND OTSU ALGORITHM HG. Sui* C. Xu^* ^ State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, PR China, xc992002@foxmail.com Commission VII, WG VII/7 KEY WORDS: Synthetic aperture radar (SAR), Multi-scale level set, Water extraction, OTSU algorithm, Segmentation ABSTRACT: Water extraction has an important significance in flood disaster management and environmental monitoring. Compared to optical sensor, Synthetic aperture radar (SAR), which has the properties of high resolution and all-weather acquisition, has been used for water extraction in this paper. Due to the presence of coherent speckles, which can be modeled as strong, multiplicative noise, water extraction in SAR image is very difficult. In order to extract water from SAR images automatically, accurately and quickly, a novel water extraction algorithm combine multi-scale level set method with OTSU algorithm is proposed in this paper. Firstly, we introduced multi-scale framework into level set method. Multi-scale framework is a method considering both global information and local information of the image. The overall structural information of the image can be maintained at coarse scales and detailed information can be kept at fine scales. Therefore, coarser scale extraction results can be used as a prior guide for the finer scale, so that not only are the statistical properties of the signal-resolution image considered, but also statistical variations of multiple resolutions are exploited. Moreover, computational complexity is reduced since much of the work can be accomplished at coarse resolutions, where there are significantly fewer pixels to process. Secondly, based on the multi-scale level set framework, the segmentation result of OTSU algorithm is used to represent the initial segmentation curve. Finally, in order to eliminate the influence of buildings shadow and road, post-processing is considered in this paper. The experiments with real SAR images demonstrate the effectiveness of the new method. 1. INTRODUCTION Synthetic Aperture Radar (SAR) has the capability of large-area coverage, cloud penetration and all-weather acquisition, and it can usually obtain massive information within a short time. Thus, they are more suitable than optical sensors to reliably and timely map inundated areas in flood situations, which usually occur under overcast sky conditions. The high resolution and the increased observation frequency of the new class of SAR sensors offer enormous potential in the domain of flood mapping. However, the improved spatial resolution of the SAR data results in a large variety of very small-scaled image objects, which makes image processing and analysis even more challenging. Segmentation is the main tool to extract water bodies from SAR images. However, there exist some difficulties: On the one hand, due to the presence of coherent speckles, which can be modeled as strong, multiplicative noise, segmentation of SAR images is generally acknowledged as a difficult problem; see Lee (1989) and Oliver and Quegan (1998). On the other hand, confused objects may influence the result of segmentation, such as buildings shadow and road. The level set method was first introduced by Osher and Sethian (1988), and since then, much effort has been directed towards image segmentation (for example: Mumford and Shah 1989, Zhao et al. 1996, Horritt 1999, Germain and Refregier 2001, Li et al. 2005, Law et al. 2008). Compared with some other SAR image segmentation methods (for example: Cook et al. 1994, * Corresponding author. E-mail address: xc992002@ foxmail.com Fjortoft et al. 1998, Xu et al. 2003), the level set method has the advantages of being robust in locating the boundary of an object, and of being able to handle topological changes in the curves during their evolution. The most general model in the level set method is the Chan- Vese (C-V) model (Chan and Vese 2001), which is based on curve evolution techniques employing the Mumford-Shah functional (Mumford and Shah 1989) for segmentation, and level sets. This model can detect objects whose boundaries are not necessarily defined by a gradient and can be adapted more easily to topological changes. Although traditional level set methods based on the C-V model have obtained encouraging results, prior information (speckle noise) of SAR images is commonly ignored. It is widely recognized that the gamma distribution is the most general model employed to represent a SAR image, thus, many authors employ the gamma statistical model instead of the C-V model to define the energy functional. For example, Martin et al. (2004) analyzed the level set implementation of region snakes based on the maximum likelihood method for different noise models, and obtained improved segmentation results. Ayed et al. (2005) investigated SAR image segmentation into a given but arbitrary number of gamma homogeneous regions via active contours and level sets. Silveira and Heleno (2009) adopted a mixture of lognormal densities for SAR image segmentation between water and land, and results demonstrated the good performance of their proposed method.

Access restriction

Copyright

fullscreen: Technical Commission VII (B7)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation