Different from convolutional neural network, transformer is able to model the long-distance relationship between the image pixels, thus it is now widely used in computer vision and remote sensing community. This paper comprehensively reviews the development of transformer models in automatic image interpretation tasks, especially the applications in image classification, object detection and semantic segmentation. Specifically, the popular transformer models are thoroughly analyzed and compared to acquire their advantages and limitations. Finally, current challenges and future works are concluded.
In this paper, we first reviewed the development of remote sensing satellites in China, subsequently establishing that artificial intelligence (AI) is the key to achieve global geographic environment change monitoring in the era of big data. Next, to improve existing specialized and isolated AI solutions, we proposed a change detection method based on the geometric registration of multi-modal images and the fusion of multi-modal radiation information. Our objective was to promote the development of artificial intelligence technology and multi-modal remote sensing image fusion technology in the field of intelligent change detection of global geographic environment.
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