航空影像
计算机科学
人工智能
航拍照片
航空影像
遥感
土地利用
上下文图像分类
计算机视觉
图像(数学)
地理
工程类
土木工程
作者
Rui Cao,Jiasong Zhu,Wei Tu,Qingquan Li,Jinzhou Cao,Bozhi Liu,Qian Zhang,Guoping Qiu
出处
期刊:Remote Sensing
[MDPI AG]
日期:2018-09-27
卷期号:10 (10): 1553-1553
被引量:102
摘要
Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.
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