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Abandoned Land Mapping Based on Spatiotemporal Features from PolSAR Data via Deep Learning Methods

文献类型： 外文期刊

作者： Yang, Yingpin 1 ; Wu, Zhifeng 1 ; Xiao, Wenju 1 ; Zhou, Ya'nan 3 ; Huang, Qiting 4 ; Wu, Tianjun 5 ; Luo, Jiancheng 6 ; Wang, Haiyun 2 ;

作者机构： 1.Guangzhou Univ, Sch Geog & Remote Sensing, Guangzhou 510006, Peoples R China

2.Minist Nat Resources, Key Lab Nat Resources Monitoring Trop & Subtrop Ar, Guangzhou 510670, Peoples R China

3.Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China

4.Guangxi Acad Agr Sci, Agr Sci & Technol Informat Res Inst, Nanning 530007, Peoples R China

5.Changan Univ, Sch Sci, Xian 710064, Peoples R China

6.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China

7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China

关键词： abandoned land identification; PolSAR; time series; LSTM; deep learning

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 16 期

页码：

收录情况： SCI

摘要： Monitoring agricultural abandonment is essential in understanding the effects on the environment and food security. Polarimetric synthetic aperture radar (PolSAR) is an efficient approach for the monitoring of large-scale agricultural land cover in cloudy and rainy areas. However, previous studies have not taken advantage of the valuable phase information and not fully utilized the spatiotemporal features of farmland parcels, which has seriously limited the abandoned land identification accuracy. In this study, we developed a new method for the mapping of abandoned land based on the spatiotemporal features from PolSAR Single Look Complex (SLC) images via deep learning methods. First, backscattering coefficients (s(0)VV, s(0)VH) were derived, and the polarimetric parameters (entropy, anisotropy and mean alpha angle) were obtained based on Cloude-Pottier polarimetric decomposition. Then, the VGG16 deep convolutional network was innovatively used to extract spatial features from both the backscattering coefficients and polarimetric parameters. Next, the separability index was calculated to select the most effective spatial features. Finally, LSTM classifications were conducted based on the time series of backscattering features, the polarimetric parameters, the extracted spatial features and their combinations. The results showed that the introduction of multitemporal polarimetric parameters and spatial features both led to an improvement in the abandoned land identification accuracy. The combination of backscattering features, polarimetric parameters and spatial features yielded the best performance in identifying abandoned land, with producer's accuracy of 88.29% and user's accuracy of 84.03%. This study demonstrated the potential of polarimetric parameters and validated the effectiveness of spatiotemporal features in abandoned land identification. It provided a practical method for the production of a highly reliable abandoned land mapping in cloudy and rainy areas.

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