Name
Title | ||
|---|---|---|
Urban Area Detection in Very High Resolution Remote Sensing Images Using Deep Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
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). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18030904 | SENSORS |
Keywords | Field | DocType |
urban area detection,remote sensing,very high resolution,deep convolutional neural networks | False positive rate,Convolutional neural network,Remote sensing,Image based,Cohen's kappa,Earth observation,Engineering,Contextual image classification,Urban area,Visual dictionary | Journal |
Volume | Issue | Citations |
18 | 3.0 | 4 |
PageRank | References | Authors |
0.45 | 30 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tian Tian | 1 | 86 | 18.09 |
| chang li | 2 | 282 | 19.50 |
| Jinkang Xu | 3 | 4 | 0.45 |
| Jiayi Ma | 4 | 1302 | 65.86 |