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An improved fully convolution network model for change detection in mining areas using sentinel-2 images
Remote Sensing Letters ( IF 1.4 ) Pub Date : 2021-05-12 , DOI: 10.1080/2150704x.2021.1925372
Chao Tang 1, 2 , Zhaoming Zhang 1 , Guojin He 1 , Tengfei Long 1 , Guizhou Wang 1 , Mingyue Wei 1, 2 , Wenqing She 1, 3
Affiliation  

ABSTRACT

High-resolution satellite images have recently been widely used for change detection of mineral resources and mining area environments. However, with their increasing resolution and number of feature sub-categories, traditional machine learning methods have become sensitive to pseudo-changes and intra-class changes, which are prone to producing misclassifications. To address these problems, a novel fully convolutional network, the Hybrid Dilated Convolutional Siamese Network (HDC-Siam) was proposed. This model combines the modular dilated convolution network with the fully convolutional Siamese networks structure, in order to reduce the commission rate. In this paper, pairs of Sentinel-2 images with an interval of about two years was used as the experimental data. The HDC-Siam model was used to detect changes, where we evaluated the accuracy in the Dongsheng coalfield in Ordos, China and the Kuznetsk coalfield in Kemerovo, Russia. We obtained F1-scores of 85% and 75% for these respective locations. In addition, we conducted comparative experiments using two other methods – Fully Convolutional Siamese – Difference (FC-Siam-diff) and Fully Convolutional Early Fusion (FC-EF) – in order to verify that the HDC-Siam works.

更新日期:2021-05-13
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