Urban Area Detection in Very High Resolution Remote Sensing Images Using Deep Convolutional Neural Networks

作者：Tian, Tian; Li, Chang; Xu, Jinkang; Ma, Jiayi^*

来源：Sensors (Switzerland), 2018, 18(3): 904.

DOI：10.3390/s18030904

• urban area detection
• remote sensing
• very high resolution
• deep convolutional neural networks

Detecting urban areas from very high resolution (VHR) remote sensing images plays an important role in the field of Earth observation. The recently-developed deep convolutional neural networks (DCNNs), which can extract rich features from training data automatically, have achieved outstanding performance on many image classification databases. Motivated by this fact, we propose a new urban area detection method based on DCNNs in this paper. The proposed method mainly includes three steps: (i) a visual dictionary is obtained based on the deep features extracted by pre-trained DCNNs; (ii) urban words are learned from labeled images; (iii) the urban regions are detected in a new image based on the nearest dictionary word criterion. The qualitative and quantitative experiments on different datasets demonstrate that the proposed method can obtain a remarkable overall accuracy (OA) and kappa coefficient. Moreover, it can also strike a good balance between the true positive rate (TPR) and false positive rate (FPR).

• 出版日期2018-3
• 单位华中科技大学; 合肥工业大学; 中国地质大学（北京）; 武汉大学