Title
Spectral–Spatial-Aware Unsupervised Change Detection With Stochastic Distances and Support Vector Machines
Abstract
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.
Year
DOI
Venue
2021
10.1109/TGRS.2020.3009483
IEEE Transactions on Geoscience and Remote Sensing
Keywords
DocType
Volume
Classification,single-class support vector machine (SVM),stochastic distance,unsupervised change detection
Journal
59
Issue
ISSN
Citations 
4
0196-2892
1
PageRank 
References 
Authors
0.35
0
7