Unsupervised deep learning based change detection in Sentinel-2 images

Sudipan Saha, Yady Tatiana Solano Correa, F. Bovolo, L. Bruzzone
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引用次数: 7

Abstract

Change Detection (CD) is an important application of remote sensing. Recent technological evolution resulted in the availability of optical multispectral sensors that provide High spatial Resolution (HR) images with many spectral bands. Such characteristics allow for new applications of CD, however present new challenges on the proper exploitation of the information. HR multitemporal data processing is challenging due to spatial correlation of pixels and spatial context information needs to be exploited to benefit from multitemporal HR images. Moreover most of the state-of-the-art CD methods exploit single or couple of spectral channels from the optical sensors to derive CD map. To overcome these challenges, this paper presents a novel unsupervised deep-learning based method that can effectively model contextual information and handle all the bands in multispectral images. In particular, we focus on the Sentinel-2 images provided by the European Space Agency (ESA) that provides both higher spatial and temporal resolution optical images with 13 spectral bands with respect to previous generation sensors. Experimental results on the urban Onera satellite CD (OSCD) dataset and on agricultural multitemporal images from Barrax, Spain confirms the effectiveness of the proposed method.
基于无监督深度学习的Sentinel-2图像变化检测
变化检测是遥感技术的一项重要应用。最近的技术发展导致了光学多光谱传感器的可用性,这些传感器可以提供具有多个光谱带的高空间分辨率(HR)图像。这些特点为CD的新应用提供了条件,但也为正确利用这些信息提出了新的挑战。人力资源多时相数据处理具有挑战性,因为需要利用像素的空间相关性和空间上下文信息来从多时相人力资源图像中获益。此外,大多数最新的CD方法利用来自光学传感器的单个或一对光谱通道来获得CD图。为了克服这些挑战,本文提出了一种新的基于无监督深度学习的方法,该方法可以有效地建模上下文信息并处理多光谱图像中的所有波段。我们特别关注欧洲航天局(ESA)提供的Sentinel-2图像,该图像提供了与上一代传感器相比具有更高空间和时间分辨率的13个光谱带光学图像。在城市Onera卫星CD (OSCD)数据集和西班牙Barrax的农业多时相图像上的实验结果证实了该方法的有效性。
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