Detecting ineligible features in agricultural fields on multitemporal high resolution satellite images using a two-stage DNN architecture

Geert W. Koster; Cornelis Valk

doi:10.1117/12.2533042

7 October 2019 Detecting ineligible features in agricultural fields on multitemporal high resolution satellite images using a two-stage DNN architecture

Geert W. Koster, Cornelis Valk

Author Affiliations +

Proceedings Volume 11155, Image and Signal Processing for Remote Sensing XXV; 111550X (2019) https://doi.org/10.1117/12.2533042
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

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.

Conference Presentation

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Geert W. Koster and Cornelis Valk "Detecting ineligible features in agricultural fields on multitemporal high resolution satellite images using a two-stage DNN architecture", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111550X (7 October 2019); https://doi.org/10.1117/12.2533042

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