In high-resolution earth observation systems, object detection in high spatial resolution remote sensing images (HSRIs) is the key technology for automatic extraction, analysis and understanding of image information. With respect to the multi-angle features of object orientation in HSRIs object detection, this paper presents a novel HSRIs object detection method based on convolutional neural networks (CNN) with adaptive object orientation features. First, an adaptive object orientation regression method is proposed to obtain object regions in any direction. In the adaptive object orientation regression method, five coordinate parameters are used to regress the object region with any direction. Then, a CNN framework for object detection of HSRIs is designed using the adaptive object orientation regression method. Using multiple object detection datasets, the proposed method is compared with some state-of-the-art object detection methods. The experimental results show that the proposed method can more accurately detect objects with large aspect ratios and densely distributed objects than some state-of-the-art object detection methods using a horizontal bounding box, and obtain better object detection results for HSRIs.

Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods.

Object detection is a key technique for automatic information extraction, analysis and understanding of high spatial resolution remote-sensing images (HSRIs). Object anchor scales are a critical factor for object detection of HSRIs based on convolutional neural networks (CNNs). With respect to adaptively obtaining optimal object anchor scales for object detection of HSRIs, this paper proposes a novel object detection method for HSRIs based on CNNs with optimal object anchor scales. First, optimal object anchor scales for object detection of HSRIs are obtained using an adaptive object-scale learning operator. Then, a CNN object detection framework for HSRIs is designed based on optimal object anchor scales. Using multiple-object detection datasets, the proposed method is compared with some state-of-the-art object detection algorithms. Experimental results show that the proposed method can achieve 90.42%, 91.98% and 85.07% mAP on WHU-RSONE, UCAS-AOD and HSRC2016, respectively, and outperforms state-of-the-art object detection algorithms.