Detection of Urban Trees in Multiple-source Aerial Data (optical, Infrared, Dsm)
نویسندگان
چکیده
Standard Remote Sensing analysis uses machine learning methods such as SVMs with HOG or SIFT descriptors, but in recent years neural networks are emerging as a key tool regarding the detection of objects. Due to the heterogeneity of remote sensing information (optical, infrared, DSM) the combination of multi-source data is still an open issue. In this paper, we focused on localization of urban trees, and we evaluate the performances of CNNs compared to standard classification methods that employ descriptor-based representation.
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تاریخ انتشار 2016