Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery
Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery
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DOI：
10.1016/j.isprsjprs.2023.11.016
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发表时间：
2023-12
影响因子：
12.7
通讯作者：
Zhu Han;Ce Zhang;Lianru Gao;Zhiqiang Zeng;Bing Zhang;Peter M. Atkinson
中科院分区：
文献类型：
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作者：
Zhu Han;Ce Zhang;Lianru Gao;Zhiqiang Zeng;Bing Zhang;Peter M. Atkinson
准确的作物制图对于作物产量预测、农业生产力发展和农业管理具有重要意义。多时相合成孔径雷达（SAR）具有全天时、全天候的特点，将其用于农作物制图已成为遥感领域的重要而又具有挑战性的任务。近年来，深度学习（DL）在解释作物动态方面表现出了出色的作物映射精度。然而，现有的基于DL的方法往往无法同时捕获不同尺度的空间和时间特征，这往往会导致严重的错误分类，由于作物的复杂性和异质性分布和不同的物候模式。在本文中，我们提出了一种新的时空多层次的注意力方法，命名为STMA，农作物映射使用时间序列SAR图像在一个端到端的方式，以增加作物物候检索的能力。具体而言，多级注意机制被设计为通过级联时空自注意（STSA）和多尺度交叉注意（MCA）模式聚合作物上的多尺度时空表示。为了保证多粒度特征的精细提取，提出了一种可学习的空间注意力位置编码来自适应地生成位置先验，以促进多级注意力学习。在勃兰登堡Sentinel-1数据集、公共PASTIS-R数据集和南非数据集上的实验结果表明，STMA在作物制图任务中具有最先进的性能，在勃兰登堡Sentinel-1数据集上的准确率为96.54%，在PASTIS-R数据集上为86.77%，在南非数据集上为83.37%，验证了其有效性和优越性。时空泛化能力的进一步比较反映了其在不同作物和场景的时空建模中的优异性能。该研究为复杂农业系统中利用时间序列SAR图像进行大面积作物制图提供了一个可行的智能时空框架。勃兰登堡Sentinel-1数据集和STMA代码将在https://github.com/hanzhu97702/ISPRS_STMA上公开。
Accurate crop mapping is of great significance for crop yield forecasting, agricultural productivity development and agricultural management. Thanks to its all-time and all-weather capability, integrating multi-temporal synthetic aperture radar (SAR) for crop mapping has become essential and challenging task in remote sensing. In recent years, deep learning (DL) has demonstrated excellent crop mapping accuracy to interpret crop dynamics. However, existing DL-based methods tend to be incapable of capturing spatial and temporal features at different scales simultaneously, and this often leads to severe mis-classification due to the complex and heterogeneous distribution of crops and diverse phenological patterns. In this paper, we propose a novel spatio-temporal multi-level attention method, named as STMA, for crop mapping using time-series SAR imagery in an end-to-end fashion to increase the capability of crop phenology retrieval. Specifically, the multi-level attention mechanism is designed to aggregate multi-scale spatio-temporal representations on crops via cascaded spatio-temporal self-attention (STSA) and multi-scale cross-attention (MCA) modalities. To ensure a fine extraction of multi-granularity features, a learnable spatial attention position encoding is proposed to adaptively generate the position priors to facilitate multi-level attention learning. Experimental results on Brandenburg Sentinel-1 dataset, public PASTIS-R dataset and South Africa dataset demonstrated that STMA can achieve state-of-the-art performance in crop mapping tasks, with the accuracy of 96.54% in the Brandenburg Sentinel-1 dataset, 86.77% in the PASTIS-R dataset and 83.37% in the South Africa dataset, validating its effectiveness and superiority. Further comparison of spatio-temporal generalization capability reflected its excellent performance in spatio-temporal modeling on different crops and scenarios. This research provides a viable and intelligent spatio-temporal framework for large-area crop mapping using time-series SAR imagery in complex agricultural systems. The Brandenburg Sentinel-1 dataset and the STMA code will be publicly available at https://github.com/hanzhu97702/ISPRS_STMA.