| Full text | |
| Author(s): |
Negri, Rogerio Galante
[1]
;
Frery, Alejandro C.
[2, 3]
;
Casaca, Wallace
[4]
;
Azevedo, Samara
[5, 6]
;
Dias, Mauricio Araujo
;
Silva, Erivaldo Antonio
[7]
;
Alcantara, Enner Herenio
[1]
Total Authors: 7
|
| Affiliation: | [1] Univ Estadual Paulista UNESP, Sci & Technol Inst, Dept Environm Engn, BR-12245000 Sao Jose Dos Campos - Brazil
[2] Univ Fed Alagoas, Lab Comp Cient & Anal Numer, BR-57072900 Maceio, Alagoas - Brazil
[3] Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071 - Peoples R China
[4] Univ Estadual Paulista UNESP, Dept Energy Engn, BR-19274000 Rosana - Brazil
[5] Univ Fed Itajuba UNIFEI, Dept Nat Resources, BR-35903087 Itajuba - Brazil
[6] Univ Estadual Paulista UNESP, Dept Math & Comp Sci, Sch Sci & Technol, BR-19060900 Presidente Prudente - Brazil
[7] Univ Estadual Paulista UNESP, Sch Sci & Technol, Dept Cartog, BR-19060900 Presidente Prudente - Brazil
Total Affiliations: 7
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING; v. 59, n. 4, p. 2863-2876, APR 2021. |
| Web of Science Citations: | 1 |
| 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. (AU) | |
| FAPESP's process: | 18/01033-3 - Research and Development of Algorithms for Change Detection in Remote Sensing Imagery |
| Grantee: | Rogério Galante Negri |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |