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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Decision Fusion of Spot6 And Multitemporal Sentinel2 Images For Urban Area Detection
摘要： Fusion of very high spatial resolution multispectral (VHR) images and lower spatial resolution image time series with more spectral bands can improve land cover classification, combining geometric and semantic advantages of both sources. This study presents a workflow to extract the extent of urban areas using decision-level fusion of individual classifications on Sentinel 2 (S2) and SPOT6 satellite images. First, both sources are classified individually in five classes, using state-of-the-art supervised classification approaches and Convolutional Neural Networks. Obtained results are merged in order to extract buildings as accurately as possible. Then, detected buildings are merged again with the S2 classification to extract urban area; a prior to be in an urban area is derived from these building objects and merged with a binary classification derived from the original S2 classification. Both fusions involve a per pixel decision level fusion followed by a contrast sensitive regularization.
关键词： Regularization,Multispectral,Segmentation,Decision fusion,Urban classification,Urban area
更新于2025-09-10 09:29:36
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Comprehensive Remote Sensing || Data- and Decision-Level Fusion for Classification
摘要： The availability of more and more data coming from remote sensors set, a few years ago, the floor to a long-standing discussion about “data fusion”, with various, sometimes very different, interpretations and perspectives. Examples of notable works presenting the state-of-the-art of remote sensing data fusion methods are Pohl and Van Genderen (1998), Dong et al. (2009), and Zhang (2010). Data fusion is indeed one of the most important steps in data processing, when multiple data sets are available. It is particularly important for remote sensing data sets, because each of them provides a spatial, temporal, or frequency-limited vision of the same target, and some kind of fusion is needed to get a more accurate picture. However, data fusion can be applied in a variety of different ways, and for different aims, some of them are still to be fully explored. This is the reason why, among the other associations, the IEEE Geoscience and Remote Sensing Society, through its Data Analysis Technical Committee, organizes Earth Observation (EO) data fusion contests on an annual basis. The outcomes of the most recent editions (Liao et al., 2015; Debes et al., 2014; Berger et al., 2013; Longbotham et al., 2012; Licciardi et al., 2009) highlight the diversity of data fusion applications with respect to research area, sensor types, and spatial resolutions.
关键词： SAR,multispectral,urban area mapping,classification,data fusion,remote sensing
更新于2025-09-10 09:29:36