Loading...
Please wait, while we are loading the content...
Similar Documents
Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery
| Content Provider | MDPI |
|---|---|
| Author | Zhang, Zhili Lu, Meng Ji, Shunping Yu, Huafen Nie, Chenhui |
| Copyright Year | 2021 |
| Description | Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. This module consists of three feature extraction sub-modules based on spatial and channel correlations in feature maps at each scale, which extract the complete target information from the local space, larger space, and between-channel relationship to achieve a rich feature representation. Simultaneously, to better predict the fine contours of water-bodies, we adopt a multi-scale prediction fusion module. Besides, to solve the semantic inconsistency of feature fusion between the encoding stage and the decoding stage, we apply an encoder-decoder semantic feature fusion module to promote fusion effects. We carry out extensive experiments in VHR aerial and satellite imagery respectively. The result shows that our method achieves state-of-the-art segmentation performance, surpassing the classic and recent methods. Moreover, our proposed method is robust in challenging water-body extraction scenarios. |
| Starting Page | 1912 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13101912 |
| Journal | Remote Sensing |
| Issue Number | 10 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-05-13 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Water-body Segmentation Multi-feature Extraction and Combination Aerial and Satellite Imagery Fully Convolutional Network |
| Content Type | Text |
| Resource Type | Article |
