Multiscale block fusion object detection method for large-scale high-resolution remote sensing imagery

Abstract

Object detection in high-spatial-resolution remote sensing images (HSRIs) is an import part of the automatic extraction and understanding of image information in high-resolution earth observation systems. Regarding how to achieve optimal block object detection for large-scale HSRIs, this paper proposes a multiscale block fusion object detection method for large-scale HSRIs. First, the objects in large-scale HSRIs are detected using different block scales, and the average precision (AP) of the different object detection results is counted at different block scales. Then, according to the statistical information, the image block scales corresponding to the optimal AP value of the different objects are obtained. Finally, a soft non-maximum suppression algorithm is used to fuse the image block scale detection results corresponding to the optimal AP values of the different objects, to obtain the object detection results of the large-scale HSRIs. The experimental results confirm that the proposed method outperforms all other single-scale image block detection methods and provides acceptable object detection results in large-scale HSRIs.