A Framework for Token-Based Scene-Classification of Remote Sensing Images

Remote sensing images often come in very high resolutions. For instance, large images of resolution 1000×1000 are quite common in remote sensing applications. Thus, one key challenge is how to effectively analysis such very high resolution images using low computational resources, while maintain a reasonable performance as appropriate to the specific problem domain, such as scene classification, object segmentation, or object detection. In this work, we propose a framework for addressing this challenge, with focus on the specific problem of scene-classification for remote-sensed aerial imagery. Various approaches have been proposed for scene-based classification of remote sensing imagery [1] , [2] , [3] . For a survey of approaches and challenges, see [4] . See also [5] .