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In view of the existing machine learning methods in the field of high -resolution remote sensing image building extraction and the like, which requires positive and negative training samples to participate at the same time, a one- class building change detection algorithm based on one -class samples without the need for negative samples is proposed. Firstly, it extracts the morphological building index features of the image, and fuse multifeatures with the spectral features. Secondly, based on the one - class classification method proposed in this paper, from the object-based perspective, it gets the object-level building change detection results. Finally, it constructs a new shape feature which is refined to obtain the final building change detection result. Through experiments on multi-source high-resolution remote sensing images, it is verified that our proposed algorithm is robust and has better detection accuracy than existing building change detection algorithms. [ABSTRACT FROM AUTHOR]

Inshore containers in high resolution optical imagery are under severe interference, such as structure, shadow, and environment, and the ship bodies are very similar to the container structures on nearby land. These situations make the automatic detection of inshore containers a very challenging task. In order to address this problem, this paper proposes a detection method for inshore containers based on the superpixelTevel contextual feature. Firstly, the image is segmented into superpixels, and the features of the superpixel and its neighboring superpixels are concatenated into the superpixel-level contextual feature. Then, based on the positive samples and the actively selected negative samples, the target and the background superpixels are classified via machine learning. Finally, the fully connected conditional random field is employed to refine the classification result and realize the detection. The experimental result verifies the applicability of the proposed method. [ABSTRACT FROM AUTHOR]