Recognizing and classifying natural or artificial geo-objects under complex geo-scenes using remotely sensed data remains a significant challenge due to the heterogeneity in their spatial distribution and sampling bias. In this study, we propose a deep learning method of surface complexity analysis based on multiscale entropy. This method can be used to reduce sampling bias and preserve entropy-based invariance in learning for the semantic segmentation of land use and land cover (LULC) images. Our quantitative models effectively identified and extracted local surface complexity scores, demonstrating their broad applicability. We tested our method using the Gaofen-2 image dataset in mainland China and accurately estimated multiscale complexity. A downstream evaluation revealed that our approach achieved similar or better performance compared to several representative state-of-the-art deep learning methods. This highlights the innovative and significant contribution of our entropy-based complexity analysis and its applicability in improving LULC semantic segmentations through optimal stratified sampling and constrained optimization, which can also potentially be used to enhance semantic segmentation under complex geo-scenes using other machine learning methods.

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Robot navigation has been a hot topic in the area of mobile robot. A lot of research has been undergone ranging from noble algorithms to applications. Fisheye image has wide angle View and it is easy to obtain the position of the object and keep it in vision within large scale, so it is an important research domain in robot navigation. However it has a disadvantage that the image obtained from fisheye has severe distortions which improve the difficulties in object detection. In this paper we combine the fisheye image and the image of the popular lens for the robot navigation based on the artificial landmark. To improve the speed we still design a continually changing window for the robust landmark tracking. Experiment shows that this hybrid method can recognize and track the landmark robustly and implement robot navigation in room environment effectively.