Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring
Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring
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DOI：
10.24963/ijcai.2019/365
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
X. Jia;Mengdie Wang;A. Khandelwal;A. Karpatne;Vipin Kumar
中科院分区：
文献类型：
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作者：
X. Jia;Mengdie Wang;A. Khandelwal;A. Karpatne;Vipin Kumar
有效和及时地监测农田对于管理粮食供应至关重要。虽然地球观测卫星的遥感数据可用于监测大面积农田，但这一任务对小规模农田具有挑战性，因为使用粗分辨率数据无法精确捕获。另一方面，较高分辨率的遥感数据采集频率较低，并且包含缺失或干扰数据。因此，传统的顺序模型不能直接应用于高分辨率数据提取时间模式，这是必不可少的识别作物。在这项工作中，我们提出了一个生成模型，结合联合收割机多尺度遥感数据检测农田在高分辨率。在学习过程中，我们利用从粗分辨率数据中学习到的时间模式来生成缺失的高分辨率数据。此外，该模型可以跟踪分类的信心在真实的时间，并可能导致早期检测。在集约化耕作区的评价表明，该方法在农田检测的有效性。
Effective and timely monitoring of croplands is critical for managing food supply. While remote sensing data from earth-observing satellites can be used to monitor croplands over large regions, this task is challenging for small-scale croplands as they cannot be captured precisely using coarse-resolution data. On the other hand, the remote sensing data in higher resolution are collected less frequently and contain missing or disturbed data. Hence, traditional sequential models cannot be directly applied on high-resolution data to extract temporal patterns, which are essential to identify crops. In this work, we propose a generative model to combine multi-scale remote sensing data to detect croplands at high resolution. During the learning process, we leverage the temporal patterns learned from coarse-resolution data to generate missing high-resolution data. Additionally, the proposed model can track classification confidence in real time and potentially lead to an early detection. The evaluation in an intensively cultivated region demonstrates the effectiveness of the proposed method in cropland detection.