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

Sensors

Chang Li¹  Jiayi Ma²  Tian Tian³  Jinkang Xu⁴ 

[1] Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China;Electronic Information School, Wuhan University, Wuhan 430072, China;Hubei Key Laboratory of Intelligent Geo-Information Processing, College of Computer Science, China University of Geosciences, Wuhan 430074, China;School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;

关键词: urban area detection;    remote sensing;    very high resolution;    deep convolutional neural networks;   

DOI  :  10.3390/s18030904

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown