Abstract:
For road extraction tasks in VHR satellite imagery, a deep neural network may perform well. But a network with certain reasoning ability as human will get a more satisfyi...Show MoreMetadata
Abstract:
For road extraction tasks in VHR satellite imagery, a deep neural network may perform well. But a network with certain reasoning ability as human will get a more satisfying result. To this end, we focus on how to effectively model the context information of the road and propose a well-designed spatial information inference structure (SIIS) which can add into any typical semantic segmentation network. The network with SIIS called SII-Net can not only learn the local visual characteristic of the road but also the global spatial structure information (such as the continuity and trend of the road). So, it can effectively solve the challenging occlusion problem in road detection and well preserve the continuity of the extracted road. The experimental results of two datasets show that the proposed method can improve the comprehensive performance of road extraction.
Date of Conference: 28 July 2019 - 02 August 2019
Date Added to IEEE Xplore: 14 November 2019
ISBN Information: