Category attention guided network for semantic segmentation of Fine-Resolution remote sensing images

Author:

Wang ShunliORCID,Hu Qingwu,Wang Shaohua,Zhao Pengcheng,Li Jiayuan,Ai MingyaoORCID

Funder

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference56 articles.

1. SegNet: a deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell.,2017

2. Chen, L.-C., Papandreou, G., Schroff, F., Adam, H., 2017. Rethinking Atrous Convolution for Semantic Image Segmentation. https://doi.org/10.48550/arXiv.1706.05587.

3. Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities;Cheng;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2020

4. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data;Diakogiannis;ISPRS J. Photogramm. Remote Sens.,2020

5. A multi-level feature fusion network for remote sensing image segmentation;Dong;Sensors,2021

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