Abstract
Change Detection in heterogeneous remote sensing images plays an increasingly essential role in many real-world applications, e.g., urban growth tracking, land use monitoring, disaster evaluation and damage assessment. The objective of change detection is to identify changes of geo-graphical entities or phenomena through two or more bitemporal images. Researchers have invested a lot in the homologous change detection and yielded fruitful results. However, change detection between heterogenous remote sensing images is still a great challenge, especially for change detection of heterogenous remote sensing images obtained from satellites and Unmanned Aerial Vehicles (UAV). The main challenges in satellite-UAV change detection tasks lie in the intensive difference of color for the same ground objects, various resolutions, the parallax effect and image distortion caused by different shooting angles and platform altitudes. To address these issues, we propose a novel method based on dual-channel fully convolution network. First, in order to alleviate the influence of differences between heterogeneous images, we employ two different channels to map heterogeneous remote sensing images from satellite and UAV, respectively, to a mutual high dimension latent space for the downstream change detection task. Second, we adopt Hough method to extract the edge of ground objects as auxiliary information to help the change detection model to pay more attention to shapes and contours, instead of colors. Then, IoU-WCE loss is designed to deal with the problem of imbalanced samples in change detection task. Finally, we conduct extensive experiments to verify the proposed method using a new Satellite-UAV heterogeneous image data set, named HTCD, which is annotated by us and has been open to public. The experimental results show that our method significantly outperforms the state-of-the-art change detection methods.
Highlights
Remote sensing image change detection refers to the process of analyzing two or more remote sensing images at different times to identify the changed geo-graphical entities or phenomena [1]
We propose a novel end-to-end deep neural network-based model to map heterogeneous images from satellite and Unmanned Aerial Vehicles (UAV) to a mutual high dimension latent space for change detection, in order to mitigate the influence of differences between heterogeneous images
Error! Reference source not found. shows that our method has a significant improvement over other methods on the heterogeneous dataset, which proves that our method is effective for change detection task between satellite and UAV remote sensing images
Summary
Remote sensing image change detection refers to the process of analyzing two or more remote sensing images at different times to identify the changed geo-graphical entities or phenomena [1]. In the application of emergency disaster evaluation and rescue, the portable UAV optical remote sensing system and high-resolution UAV images are still the most effective source information of the target area. Satellite-UAV heterogeneous remote sensing image change detection can be regarded as a typical imbalanced learning problem. To address these difficulties in heterogeneous change detection, we propose a novel heterogeneous change detection approach and conduct comprehensive experiments on a new dataset we collect and annotate. The main contributions of this paper are as follows: We propose a novel end-to-end deep neural network-based model to map heterogeneous images from satellite and UAV to a mutual high dimension latent space for change detection, in order to mitigate the influence of differences (color, resolution, parallax and image distortion) between heterogeneous images.
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