Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information From Time-Series SAR Images for Paddy Rice Mapping

文献类型： 外文期刊

第一作者： Yang, Lingbo

作者： Yang, Lingbo;Huang, Jingfeng;Mijiti, Ruzemaimaiti;Wei, Pengliang;Tang, Chao;Huang, Ran;Lin, Tao;Wang, Limin;Shao, Jie;Li, Qiangzi;Du, Xin

作者机构：

关键词： Feature extraction; Agriculture; Data models; Satellites; Deep learning; Radar polarimetry; Remote sensing; Data augmentation; feature visualization; generalization ability; temporal feature-based segmentation (TFBS); time-series images

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.125； 五年影响因子：8.137 ）

ISSN： 0196-2892

年卷期： 2022 年 60 卷

页码：

收录情况： SCI

摘要： Synthetic aperture radar (SAR) can be used to obtain remote sensing images of different growth stages of crops under all weather conditions. Such time-series SAR images can provide an abundance of temporal and spatial features for use in large-scale crop mapping and analysis. In this study, we propose a temporal feature-based segmentation (TFBS) model for accurate crop mapping using time-series SAR images. This model first extracts deep-seated temporal features and then learns the spatial context of the extracted temporal features for crop mapping. The results indicate that the TFBS model significantly outperforms traditional long short-term memory (LSTM), U-network, and convolutional LSTM models in crop mapping based on time-series SAR images. TFBS demonstrates better generalizability than other models in the study area, which makes it more transferable, and the results show that data augmentation can significantly improve this generalizability. The visualization of the temporal features extracted by the TFBS shows that there is a high degree of intraclass homogeneity among rice fields and interclass heterogeneity between rice fields and other features. TFBS also achieved the highest accuracy of the four deep learning models for multicrop classification in the study area. This study presents a feasible way of producing high-accuracy large-scale crop maps based on the proposed model.

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