Abstract
This paper presents a novel approach for automatically detecting land cover changes from multitemporal high-resolution remote sensing images in the deep feature space. This is accomplished by using multitemporal deep feature collaborative learning and a semi-supervised Chan–Vese (SCV) model. The multitemporal deep feature collaborative learning model is developed to obtain the multitemporal deep feature representations in the same high-level feature space and to improve the separability between changed and unchanged patterns. The deep difference feature map at the object-level is then extracted through a feature similarity measure. Based on the deep difference feature map, the SCV model is proposed to detect changes in which labeled patterns automatically derived from uncertainty analysis are integrated into the energy functional to efficiently drive the contour towards accurate boundaries of changed objects. The experimental results obtained on the four data sets acquired by different high-resolution sensors corroborate the effectiveness of the proposed approach.
Highlights
Land cover change information is extremely important for the study of global climate change, biodiversity, environmental monitoring, and national resources management [1,2,3,4]
Post-classification comparison is performed on multitemporal images to independently classify pixels, and the classified maps are compared for change analysis [11,12,13]
We propose a novel change detection (CD) framework which combines deep feature learning (DFL) and a novel semi-supervised CV (SCV) model for detecting changes from multitemporal high-resolution remote sensing images
Summary
Land cover change information is extremely important for the study of global climate change, biodiversity, environmental monitoring, and national resources management [1,2,3,4]. Direct comparison approaches based on the difference feature map have been widely implemented to automatically detect changes from multitemporal remote sensing data without the need for any prior information. Zhang et al [51] utilized feature learning based on deep neural networks and mapping transformation for CD from images with different spatial resolutions. We propose a novel CD framework which combines deep feature learning (DFL) and a novel semi-supervised CV (SCV) model for detecting changes from multitemporal high-resolution remote sensing images. The SCV algorithm is proposed to detect the changed objects which can automatically exploit seed patterns with labeling information to guide the level set evolution This CD procedure does not require any prior information.
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