Spectral-Spatial-Aware Unsupervised Change Detection with Stochastic Distances and Support Vector Machines
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Data
2021-04-01
Autores
Negri, Rogerio Galante [UNESP]
Frery, Alejandro C.
Casaca, Wallace [UNESP]
Azevedo, Samara
DIas, Mauricio Araujo [UNESP]
Silva, Erivaldo Antonio [UNESP]
Alcantara, Enner Herenio [UNESP]
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Resumo
Change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods.
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Classification, single-class support vector machine (SVM), stochastic distance, unsupervised change detection
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IEEE Transactions on Geoscience and Remote Sensing, v. 59, n. 4, p. 2863-2876, 2021.