A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation
文献类型：期刊论文
| 作者 | Zhang, Gang1; Lei, Tao; Cui, Yi; Jiang, Ping |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2019-12-01 |
| 卷号 | 8期号:12 |
| 关键词 | remote sensing convolutional neural networks high-resolution aerial images semantic segmentation semantic boundaries lightweight network |
| DOI | 10.3390/ijgi8120582 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two challenges: intra-class heterogeneity and inter-class homogeneity. To overcome these two problems, a novel dual-path DCNN, which contains a spatial path and an edge path, is proposed for high-resolution aerial image segmentation. The spatial path, which combines the multi-level and global context features to encode the local and global information, is used to address the intra-class heterogeneity challenge. For inter-class homogeneity problem, a Holistically-nested Edge Detection (HED)-like edge path is employed to detect the semantic boundaries for the guidance of feature learning. Furthermore, we improve the computational efficiency of the network by employing the backbone of MobileNetV2. We enhance the performance of MobileNetV2 with two modifications: (1) replacing the standard convolution in the last four Bottleneck Residual Blocks (BRBs) with atrous convolution; and (2) removing the convolution stride of 2 in the first layer of BRBs 4 and 6. Experimental results on the ISPRS Vaihingen and Potsdam 2D labeling dataset show that the proposed DCNN achieved real-time inference speed on a single GPU card with better performance, compared with the state-of-the-art baselines. |
| 出版地 | BASEL |
| WOS研究方向 | Geography, Physical ; Remote Sensing |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000518041800061 |
| 源URL | [http://ir.ioe.ac.cn/handle/181551/9720]  |
| 专题 | 光电技术研究所_光电探测技术研究室（三室） |
| 作者单位 | 1.Chinese Acad Sci, Inst Opt & Elect, POB 350,1 Guangdian Ave, Chengdu 610209, Peoples R China 2.Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Gang,Lei, Tao,Cui, Yi,et al. A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2019,8(12). |
| APA | Zhang, Gang,Lei, Tao,Cui, Yi,&Jiang, Ping.(2019).A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,8(12). |
| MLA | Zhang, Gang,et al."A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 8.12(2019). |
入库方式： OAI收割
来源：光电技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。