Hybridizing Cross-Level Contextual and Attentive Representations for Remote Sensing Imagery Semantic Segmentation
Open Access
- 29 July 2021
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 13 (15), 2986
- https://doi.org/10.3390/rs13152986
Abstract
Semantic segmentation of remote sensing imagery is a fundamental task in intelligent interpretation. Since deep convolutional neural networks (DCNNs) performed considerable insight in learning implicit representations from data, numerous works in recent years have transferred the DCNN-based model to remote sensing data analysis. However, the wide-range observation areas, complex and diverse objects and illumination and imaging angle influence the pixels easily confused, leading to undesirable results. Therefore, a remote sensing imagery semantic segmentation neural network, named HCANet, is proposed to generate representative and discriminative representations for dense predictions. HCANet hybridizes cross-level contextual and attentive representations to emphasize the distinguishability of learned features. First of all, a cross-level contextual representation module (CCRM) is devised to exploit and harness the superpixel contextual information. Moreover, a hybrid representation enhancement module (HREM) is designed to fuse cross-level contextual and self-attentive representations flexibly. Furthermore, the decoder incorporates DUpsampling operation to boost the efficiency losslessly. The extensive experiments are implemented on the Vaihingen and Potsdam benchmarks. In addition, the results indicate that HCANet achieves excellent performance on overall accuracy and mean intersection over union. In addition, the ablation study further verifies the superiority of CCRM.Funding Information
- National Key Research and Development Program of China (2018YFC0407105, 2018YFC0407905, 2017YFC0405505)
- Technology Project of China Huaneng Group (MW 2017/P28, 51779100, 51679103, HKY-JBYW-2020-21, HKY-JBYW-2020-07)
This publication has 41 references indexed in Scilit:
- Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural NetworksIEEE Transactions on Geoscience and Remote Sensing, 2018
- Semantic labeling in very high resolution images via a self-cascaded convolutional neural networkISPRS Journal of Photogrammetry and Remote Sensing, 2018
- CBAM: Convolutional Block Attention ModulePublished by Springer Science and Business Media LLC ,2018
- Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided FiltersRemote Sensing, 2018
- Classification with an edge: Improving semantic image segmentation with boundary detectionISPRS Journal of Photogrammetry and Remote Sensing, 2018
- DECISION SUPPORT SYSTEM BASED ON ARTIFICIAL INTELLIGENCE, GIS AND REMOTE SENSING FOR SUSTAINABLE PUBLIC AND JUDICIAL MANAGEMENTEuropean Journal of Sustainable Development, 2017
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the ArtIEEE Geoscience and Remote Sensing Magazine, 2016
- U-Net: Convolutional Networks for Biomedical Image SegmentationPublished by Springer Science and Business Media LLC ,2015