计算机科学
人工智能
分割
变更检测
人工神经网络
卫星
图像分辨率
目标检测
计算机视觉
遥感
图像分割
像素
模式识别(心理学)
地理
工程类
航空航天工程
作者
Geert W. Koster,C.F. de Valk
摘要
Agricultural fields are monitored for the purpose of EU subsidy eligibility checks. A precondition to make automatic monitoring of fields for this purpose possible is that the object geometric boundary is correct. This precondition can be addressed to some extent by performing image time series analysis to identify changes. Accurate object change detection in agricultural fields on satellite images requires separating object class changes such as new ditches, buildings, or roads from other changes, such as crop development, crop management practices, seasonal variation, or shadows from adjacent objects. In this paper we present an approach to identify unchanged agricultural fields using Deep Neural Networks. We propose a combination of CNNs for semantic segmentation and ConvLSTMs for change detection, applied to multitemporal satellite image time-series of arbitrary length. The neural networks were trained on images acquired over the Netherlands in 2017 by the TripleSat and PlanetScope constellations (0.8 and 3.5m resolution respectively) with RGB and NIR bands. We introduce techniques to create artificial change training data, reducing the need for real training data. The results demonstrate that (1) a neural network is not required to be deep to achieve usable semantic segmentation performance for satellite images for this application, that (2) ConvLSTMs can to some extent compensate imperfect image alignment and pixel misclassification, that (3) longer time series significantly increase the performance of the change detection and that (4) expanding and densifying the time series with lower resolution imagery does not improve accuracy in this particular configuration.
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