Random Region Matting for the High-Resolution PolSAR Image Semantic Segmentation
| Autor: | Jun Ni, Qiang Yin, Fan Zhang, Fei Ma, Deliang Xiang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Synthetic aperture radar
Atmospheric Science Data augmentation Computer science Feature extraction Geophysics. Cosmic physics 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 0202 electrical engineering electronic engineering information engineering Preprocessor Segmentation Computers in Earth Sciences TC1501-1800 021101 geological & geomatics engineering polarimetric synthetic aperture radar (PolSAR) Intersection (set theory) business.industry QC801-809 Pattern recognition Image segmentation Filter (signal processing) Data truncation semantic segmentation Ocean engineering 020201 artificial intelligence & image processing Artificial intelligence business Gaofen-3 image classification |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 3040-3051 (2021) |
| ISSN: | 2151-1535 |
| Popis: | Polarimetric synthetic aperture radar (PolSAR) imagery can provide more intuitive and detailed SAR polarization information, and it is widely used in the classification and semantic segmentation of remote sensing. To bridge the PolSAR data and application, the 2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation provides a set of high-quality PolSAR semantic segmentation dataset. A series of preprocessing methods is first used to analyze the PolSAR images to improve the semantic segmentation performance of the PolSAR imagery. A special polarimetric decomposition method is used to extract the features, and the filter and the data truncation are implemented to enhance local and global information of images. And the random region matting method is proposed to expand the training samples. Finally, the DeepLabV3+ method with the ResNet101-V2 is employed to achieve the semantic segmentation. A variety of comparison experiments verifies the effectiveness of our methods. Simultaneously, compared with the classification methods of other groups in the competition, our methods have obvious advantages in the inference time and semantic segmentation accuracy. The proposed method achieved a frequency weighted intersection over union of 75.29% in the contest. |
| Databáze: | OpenAIRE |
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