Semantic Segmentation of High Resolution Remote Sensing Images with Extra Context Attention Mechanism
| Autor: | Mei Xie, Fu Weifu, Wang Shicheng, Yanxiang Gong, Peng Qing, Li Feng |
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| Rok vydání: | 2020 |
| Předmět: |
Pixel
Channel (digital image) Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Context (language use) 02 engineering and technology Image segmentation Discriminative model Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation 021101 geological & geomatics engineering Remote sensing |
| Zdroj: | ICCT |
| Popis: | High Resolution Remote Sensing Images (HRRSIs) usually have a larger size compared with natural images. Because of the limitation of GPU memory, it is not possible to train semantic segmentation models on HRRSIs directly. Commonly used methodologies perform training and prediction on cropped sub-images. Thus they fail to model potential dependencies between pixels beyond sub-images. To solve this problem, we firstly propose extra context attention to capture global information from larger receptive fields and discriminative information from surrounding pixels beyond sub-images. Secondly, we apply feature map refinement module to better fuse extra context information and primary semantic information. Finally, we apply channel attention module to improve the performance of the decoder so that features from different levels can be better integrated. Experimental results on ISPRS Potsdam dataset demonstrate the effectiveness of our proposed network for semantic segmentation in HRRSIs. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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