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 |
Name | Order | Citations | PageRank |
---|---|---|---|
Rogério Galante Negri | 1 | 6 | 4.31 |
Alejandro C. Frery | 2 | 368 | 38.29 |
Wallace Casaca | 3 | 88 | 10.04 |
Samara Calçado de Azevedo | 4 | 1 | 0.35 |
Maurício Araújo Dias | 5 | 1 | 1.37 |
erivaldo antonio da unesp silva | 6 | 4 | 2.85 |
Enner Alcântara | 7 | 8 | 3.97 |