Exploring the effectiveness of various texture features for change detection in remote sensing images

Khurana, Madhu; Saxena, Vikas

doi:10.1109/comptelix.2017.8003945

Cited by 3 publications

(1 citation statement)

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“…Besides, texture can depress the effect of SAR image speckle noise. Accordingly, texture features along with spectral and morphological operators have been shown to have a positive impact on the accuracy of detection in remote sensing images (Khurana & Saxena, 2017;Wu et al, 2017;Zhuang, Deng, & Fan, 2016). Li, Huang, Wen, and Liu (2017) proposed a multi-texture change detection method by integrating macro-and micro-texture features.…”

Section: Introductionmentioning

confidence: 99%

A correlation change detection method integrating PCA and multi- texture features of SAR image for building damage detection

Gong

Zhang

2019

European Journal of Remote Sensing

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Synthetic Aperture Radar (SAR) data are used to map affected urban areas after an earthquake generally exploiting change detection techniques. In this paper, a novel change detection approach is proposed for multitemporal (SAR) images. The approach is based on two different parts, which are constructed through principal components (PCs) of multiple textural features and correlation analysis image, respectively. In the extraction of the PCs of texture, the principal component analysis technique is used to separate irrelevant and noisy elements. Then, correlation change detection (CCD) is carried out to detect the changes associated with the building collapse. Considering the earthquake that hit L'Aquila city (Italy) on 6 April 2009, the effectiveness of the proposed method is proved by the experiment results obtained on real SAR images data sets collected before and after the seismic event.

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Section: Introductionmentioning

confidence: 99%