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Sensors 2019, 19(1), 79; https://doi.org/10.3390/s19010079

Object-Level Double Constrained Method for Land Cover Change Detection

1,2,†
,
1,*
,
1,†
and
1,2
1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 22 November 2018 / Revised: 13 December 2018 / Accepted: 21 December 2018 / Published: 26 December 2018
(This article belongs to the Special Issue Advances in Remote Sensing of Land-Cover and Land-Use Changes)
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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. View Full-Text
Keywords: land cover change detection; object-level; multi-scale segmentation; optimal features selection; change vector analysis; correlation coefficient land cover change detection; object-level; multi-scale segmentation; optimal features selection; change vector analysis; correlation coefficient
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Wang, Z.; Liu, Y.; Ren, Y.; Ma, H. Object-Level Double Constrained Method for Land Cover Change Detection. Sensors 2019, 19, 79.

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