Region-Enhanced Convolutional Neural Network for Object Detection in Remote Sensing Images
Jianjun Lei, Xiaowei Luo, Leyuan Fang, Mengyuan Wang, Yanfeng Gu
Abstract
The convolutional neural networks (CNNs) have recently demonstrated to be a powerful tool for object detection. However, with the complex scenes in remote sensing images, feature extraction of the object in the CNN will be seriously affected by background information. To address this issue, in this article, a region-enhanced CNN (RECNN) is proposed for the object detection of remote sensing images. The RECNN introduces the saliency constraint and multilayer fusion strategy into the CNN model, which can effectively enhance the object regions for better detection. Specifically, the saliency map is extracted and utilized to guide the training of the proposed model to strengthen saliency regions in feature maps. In addition, since different layers can reflect the object regions in varied resolutions, a multilayer fusion strategy is introduced to connect different convolutional layers and explore the context, where the feature maps of object regions are further enhanced. Experimental results on a publicly available ten-class object detection data set demonstrate the superiority of the RECNN over several competitive object detection methods.