Identification of Burned Areas Based on Texture Features of Different Windows. Issue 1 (1st December 2022)

• Record Type: Journal Article
• Authors: Wu, Jinwen; Zhang, Yushu; Sun, Longyu; Ji, Ruipeng; Yu, Wenying; Feng, Rui
• Is Part Of: Journal of physics. Volume 2410 Issue 1(2022)
• Publication Date: 2022-12-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2410/1/012012
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: It is vital to choose appropriate pixel spacing as the window size determines the distribution of texture features in a gray-level co-occurrence matrix (GLCM). The window size ranged from 9×9 to 15×15 to keep the richness of texture image information. In this study, the importance evaluation method based on the Gini index was used to determine the appropriate window for the identification of burned areas, and burned areas were identified with the random forest classification method based on texture features. The results showed that 15×15 was the most appropriate texture window size for the identification of burned areas, and the best texture features for the identification of burned areas were the contrast of the visible blue light band and the texture mean of the near-infrared band; burned areas could be fully identified with the random forest classification method based on texture features, with an overall classification accuracy of 83.87%, but there would be interference pixels in reservoirs, pits, ponds, marshes and irrigated dry fields.