A Enhanced DeepLabv3+ for PolSAR image classification-Reference-Cited by-同舟云学术

A Enhanced DeepLabv3+ for PolSAR image classification

Published:2023-04-07 Issue: Volume: Page:
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Container-title:2023 4th International Conference on Computer Engineering and Application (ICCEA)
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Author:

Zhang Fang¹,Li Pin²,Zhang Yuanpeng¹,Liu Xiaolu¹,Ma Xiaojing¹,Yin Zhiping¹

Affiliation:

1. Hefei University of Technology,National Engineering Laboratory of Special Display Technology, Academy of Opto-Electric Technology,Hefei,China,230009

2. Nanjing Research Institute of Electronics Technology,Nanjing,China,210039

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10134761/10135179/10135214.pdf?arnumber=10135214

Reference8 articles.

1. A Lightweight Complex-Valued DeepLabv3+ for Semantic Segmentation of PolSAR Image

2. Identification of maize leaf diseases by using the SKPSNet-50 convolutional neural network model

3. Instance segmentation of point cloud captured by RGB-D sensor based on deep learning

4. GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

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1. Research on Input Schemes for Polarimetric SAR Classification Using Deep Learning;Remote Sensing;2024-05-21

2. Weighted Features Based Classification of Polarimetric SAR Images;2024 13th Iranian/3rd International Machine Vision and Image Processing Conference (MVIP);2024-03-06

3. Polarimetry-Inspired Contrastive Learning for Class-Imbalanced PolSAR Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024