Key point extraction method for spatial objects in high-resolution remote sensing images based on multi-hot cross-entropy loss

Abstract

Abstract Extracting spatial objects and their key points from remote sensing images has attracted great attention of worldwide researchers in intelligent machine perception of the Earth’s surface. However, the key points of spatial objects (KPSOs) extracted by the conventional mask region-convolution neural network model are difficult to be sorted reasonably, which is a key obstacle to enhance the ability of machine intelligent perception of spatial objects. The widely distributed artificial structures with stable morphological and spectral characteristics, such as sports fields, cross-river bridges, and urban intersections, are selected to study how to extract their key points with a multihot cross-entropy loss function. First, the location point in KPSOs is selected as one category individually to distinguish morphological feature points. Then, the two categories of key points are arranged in order while maintaining internal disorder, and the mapping relationship between KPSOs and the prediction heat map is improved to one category rather than a single key point. Therefore, the predicted heat map of each category can predict all the corresponding key points at one time. The experimental results demonstrate that the prediction accuracy of KPSOs extracted by the new method is 80.6%, taking part area of Huai’an City for example. It is reasonable to believe that this method will greatly promote the development of intelligent machine perception of the Earth’s surface.