A Fine-Grained Fully Convolutional Network For Extraction of Building Along High-Speed Rail Lines from VHR Remote Sensing Image

Abstract

Very high resolution (VHR) remote sensing offers the potential for efficient identification of building areas along the high-speed rail (HSR) lines. However, high intra-class and low inter-class variances in this type of images and pose serious challenges. Fully convolutional network (FCN) has shown impressive performance and great potential for image classification and object detection. Nevertheless, only classification results with coarse resolutions can be obtained from traditional FCN-based methods. To address this issue, this paper presents a fine-grained fully convolutional network to conduct the building extraction task along the HSR lines. The proposed method introduce a fine-grained upsampling module in the encoder-decoder paradigm to upsample the low-resolution feature maps to higher resolution ones instead of the traditional interpolation processing, thus can reserve and recover more fine-detailed information. Furthermore, an enhanced loss function is designed to impose smoothness prior for building extraction. Experiments on the remote sensing image with 0.5 m spatial resolution from Google Earth covering the part area of Zhengzhou-Xi’an high-speed rail line indicates the proposed method achieve competitive results compared with other state-of-the-art FCN based methods. The study demonstrates the potential application of using high-resolution remote sensing technique to investigate the building areas on both sides of HSR lines regularly, in order to detect the hazardous building areas.