Abstract
Land cover change detection based on remote sensing has become increasingly important for protecting the ecological environment. Spatial features of images can be extracted by object-level methods. However, the computational complexity is high when using many features to detect land cover change. Meanwhile, single-constrained change detection (SCCD) methods produce non-objective and inaccurate results. Therefore, we proposed a land cover change detection method: the object-level double constrained change detection (ODCD) method. First, spectral and spatial features were calculated based on multi-scale segmentation results. Second, using the significant difference test (SDT), feature differences among all categories were calculated, and the features with more significant differences were considered as the optimal features. Third, the maximum Kappa coefficient was used as the criterion for determining the optimal change intensity and correlation coefficient. Finally, the ODCD was validated using GF-1 satellite images on March 2016 and February 2017 in north Beiqijia Town, Beijing. Using optimal feature selection, the dimension of features was reduced from 26 to 12. Compared with SCCD methods, the result of the ODCD was more reliable and accurate. Its overall accuracy was 10% higher, overall error was 27% lower, and the Kappa coefficient was 0.22 higher. In conclusion, the ODCD is effective for land cover change detection and can improve computational efficiency.
Highlights
Owing to the rapid increase of urban populations and the rapid expansion of urban areas, many ecological and environmental problems, such as reduced vegetation cover and increased surface runoff, have become gradually more serious [1]
This study proposed a method of double constrained thresholds for the change intensity threshold and the correlation coefficient on the object-level (ODCD), which aimed to reduce the number of dimensions for features and to improve the computational efficiency, objectivity, and accuracy of land cover change detection
When single-constrained change detection (SCCD) was applied for land cover change detection, only the change intensity was used
Summary
Owing to the rapid increase of urban populations and the rapid expansion of urban areas, many ecological and environmental problems, such as reduced vegetation cover and increased surface runoff, have become gradually more serious [1]. As the core of ecological environment change monitoring, land cover change detection has become a hot topic in environmental science and ecology [2]. Remote sensing technology has the advantages of being macroscopic, comprehensive, dynamic, and rapid, as well as being the most economical and effective means for detecting land cover changes [3]. Various remote-sensing methods have been applied to this problem: Yuan et al used Principal Component. Guizhou Province [6]; and Li and Ye used PCA to detect changes in Dongguan, Zhujiang. These methods were all on the pixel-level; they cannot use the spatial characteristics of images and are prone to the serious “pepper and salt phenomenon” [8]. Due to Sensors 2019, 19, 79; doi:10.3390/s19010079 www.mdpi.com/journal/sensors
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