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/6: REMOTE SENSING DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: A TRANSFORMATION METHOD FOR TEXTURE FEATURE DESCRIPTION UNDER DIFFERENT IMAGINE CONDITIONS Z. Guan, J. Yu, T. Feng , A. Li

Document type:: Multivolume work

Structure type:: Chapter

cient addi- ssifications let and the isual qual- sults based K brightness pectral sig- 1. In order ose to per- atic image, n a second y the inter- which are parison of it rankings >thod has a purposes. d truth in- ods. They isclassified inal scales. 16 , M. 2005: auflósende ymposium, ancing the harpening. New York). g. Elsevier age fusion 1s. Interna- - 854, Mar. nd spectral mentation. 6(1), 49-61 ality Index. 2002 /software/- 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 A TRANSFORMATION METHOD FOR TEXTURE FEATURE DESCRIPTION UNDER DIFFERENT IMAGINE CONDITIONS Z. Guan, J. Yu, T. Feng , A. Li Research Center of RS & Spatial Infor. Technology /Department of Surveying and Geo-informatics, College of Civil Engineering, Tongji University (zequnguan, 2011_jieyu,fengtiantian)@tongji.edu.cn asia.aixia@gmail.com Commission VII, Working Group VII/6 KEY WORDS: Transformation method, Texture feature, Gabor wavelet, Gaussian mixture models ABSTRACT: For high spatial resolution Remote Sensing images, it is very important to investigate the transformational methods between background and target characteristics. Only in this way rich details in images under different imaging conditions can be well extracted. Amongst the characteristics of imagery targets, texture is a visual feature that reflects the homogeneity of images and the inner attributes of different objects. What’s more, it includes important information which describes the structural arrangement of objects and the connection with the surrounding environment. This paper regards texture as the major feature and investigates the transformational methods of texture feature description under different imaging conditions. This paper mainly consists of three parts:(1) Construct a wavelet filter based on Gabor wavelet, which describes texture features obtained under different imaging conditions;(2) Process and analyze the different object’s texture features jointly by the relationship which is built by the wavelet description;(3) Build the transformation between the wavelet descriptions of the different object’s texture features based on the characteristics of the band and direction. 1. INTRODUCTION As a natural attribute of subjects, texture is a visual feature that reflects the homogeneity of images. People have researched image texture for more than 50 years and formed many methods of texture features description under different imaging conditions. There are several approaches to multispectral texture description, both supervised and unsupervised. Haralick (1973) presented a new method called gray level co-occurrence matrix(GLCM) which is now widely used. He applied GLCM to Landsat-1 multispectral image of the California coastal area to solve the land use problem. Weszk et al. (1976) researched texture for terrain analysis by using first-order statistics of gray level differences and second-order gray level statistics. Lopez- Espinoza et al. (2008) presented a method for image classification which was taken by SPOT-5 and TM, based on tree-structured Markov random field (TS-MRF) and a texture energy function (TEF). Chellappa et al. (1985) applied Gaussian Markov random field (GMRF) models to image classification. Pentland (1984) put forward that fractals can be used in the area of texture features description. Shu (1998) described the SPOT image of Wuhan based on the method of fractal assessment in image texture analysis. Chitre and Dhawan (1999) used multi band wavelet for natural texture classification. People began to research SAR image texture after the first radar satellite running in earth orbit launched successfully by America. Soh and Tsatsoulis (1999) used GLCM to analyze sea ice texture with 100-m ERS-1 synthetic aperture radar (SAR) imagery. Duskunovic et al. (2000) detected urban areas with the Markov Random Field (MRF) texture classification in SAR imagery. Hu et al. (2001) extracted texture information from Radarsat imagery of Xuzhou with Daubechies3 orthogonal wavelet successfully. Ni et al. (2004) used orthogonal wavelet and second generation wavelet for SAR image classification and compared the results from different methods with each other. Ivanov and Paschenko (2006) studied SAR image segmentation based on fractal dimension field. People also have researched infrared imagery texture description recently. Song, Wan and Chen (2006) applied GLCM to TM6 infrared imagery for image enhancement by computing six textural features. Lin et al. (2009) established the detection probability model based on texture feature of thermal infrared image with Gabor wavelet. All of the above methods of texture features description under different imaging conditions can be divided into five categories according to the principle proposed (Tuceryan, Jain, 1993). They are statistical methods, geometrical methods, structural methods, model based methods and signal processing methods. Both GLCM and Gabor wavelet are the most popular methods among all of them. And this paper will research the investigating transformational methods of texture features description under different imaging conditions based on Gabor wavelet. The wavelet theory has been utilized in image texture analysis since it was introduced into the area of image process (Mallat, 1989). And among all branches developed from wavelet, Gabor wavelet has been proved to be the optical filter of both spatial domain and frequency domain under 2D uncertainty. Dunn, Higgins and Wakeley (1994) devised a more rigorous method for designing 2D Gabor filters and utilized it to segment images. Wu et al. (2001) designed a optimal Gabor filter for Bi-textured image segmentation with the Fourier power spectrum density. Chen and Wang (2007) integrated Gabor wavelet and independent component analysis (ICP) for image classification. Clausi and Jernigan (2000) used Gabor filters to classify a SAR aerial image and obtain textures from the Brodatz album based on human visual system (HVS). Arivazhagan et al. (2006) proposed a method of image classification using Gabor filter based on rotation invariant features. Also Gabor wavelet has been utilized in other areas. Lin et al. (2007) evaluated the result of camouflage with Gabor wavelet. Song, Liu and Xie

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