An Energy-Based Model Encoding Nonlocal Pairwise Pixel Interactions for Multisensor Change Detection

Image change detection (CD) is a challenging problem, particularly when images come from different sensors. In this paper, we present a novel and reliable CD model, which is first based on the estimation of a robust similarity-feature map generated from a pair of bitemporal heterogeneous remote sens...

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Published in:	IEEE transactions on geoscience and remote sensing Vol. 56; no. 2; pp. 1046 - 1058
Main Authors:	Touati, Redha, Mignotte, Max
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Change detection Change detection (CD) Data acquisition Detection energy-based model Estimation FastMap Feature maps fusion of binary segmentations heterogeneous sensors Image acquisition Image detection Image processing Image segmentation Image sensors Interactions Mathematical models MDS Multidimensional scaling multidimensional scaling (MDS) mapping multimodal remote sensing multisensors multisource data Optical sensors pairwise pixel interactions Pixels Remote sensing Robustness Satellite imagery Satellites Scaling Similarity Synthetic aperture radar
ISSN:	0196-2892, 1558-0644
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Abstract	Image change detection (CD) is a challenging problem, particularly when images come from different sensors. In this paper, we present a novel and reliable CD model, which is first based on the estimation of a robust similarity-feature map generated from a pair of bitemporal heterogeneous remote sensing images. This similarity-feature map, which is supposed to represent the difference between the multitemporal multisensor images, is herein defined, by specifying a set of linear equality constraints, expressed for each pair of pixels existing in the before-and-after satellite images acquired through different modalities. An estimation of this overconstrained problem, also formulated as a nonlocal pairwise energy-based model, is then carried out, in the least square sense, by a fast linear-complexity algorithm based on a multidimensional scaling mapping technique. Finally, the fusion of different binary segmentation results, obtained from this similarity-feature map by different automatic thresholding algorithms, allows us to precisely and automatically classify the changed and unchanged regions. The proposed method is tested on satellite data sets acquired by real heterogeneous sensor, and the results obtained demonstrate the robustness of the proposed model compared with the best existing state-of-the-art multimodal CD methods recently proposed in the literature.
AbstractList	Image change detection (CD) is a challenging problem, particularly when images come from different sensors. In this paper, we present a novel and reliable CD model, which is first based on the estimation of a robust similarity-feature map generated from a pair of bitemporal heterogeneous remote sensing images. This similarity-feature map, which is supposed to represent the difference between the multitemporal multisensor images, is herein defined, by specifying a set of linear equality constraints, expressed for each pair of pixels existing in the before-and-after satellite images acquired through different modalities. An estimation of this overconstrained problem, also formulated as a nonlocal pairwise energy-based model, is then carried out, in the least square sense, by a fast linear-complexity algorithm based on a multidimensional scaling mapping technique. Finally, the fusion of different binary segmentation results, obtained from this similarity-feature map by different automatic thresholding algorithms, allows us to precisely and automatically classify the changed and unchanged regions. The proposed method is tested on satellite data sets acquired by real heterogeneous sensor, and the results obtained demonstrate the robustness of the proposed model compared with the best existing state-of-the-art multimodal CD methods recently proposed in the literature.
Author	Touati, Redha Mignotte, Max
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SubjectTerms	Algorithms Change detection Change detection (CD) Data acquisition Detection energy-based model Estimation FastMap Feature maps fusion of binary segmentations heterogeneous sensors Image acquisition Image detection Image processing Image segmentation Image sensors Interactions Mathematical models MDS Multidimensional scaling multidimensional scaling (MDS) mapping multimodal remote sensing multisensors multisource data Optical sensors pairwise pixel interactions Pixels Remote sensing Robustness Satellite imagery Satellites Scaling Similarity Synthetic aperture radar
Title	An Energy-Based Model Encoding Nonlocal Pairwise Pixel Interactions for Multisensor Change Detection
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