Name
Abstract | ||
|---|---|---|
Large-scale Landsat image classification is essential for the production of land cover maps. The rise of convolutional neural networks (CNNs) provides a new idea for the implementation of Landsat image classification. However, pixels in Landsat images have higher uncertainty compared with high-resolution images due to its 30-m spatial resolution. In addition, the current deep learning methods tend... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LGRS.2019.2890996 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Remote sensing,Earth,Artificial satellites,Entropy,Uncertainty,Training,Forestry | Cross entropy,Computer vision,Convolutional neural network,Markov chain,Artificial intelligence,Pixel,Deep learning,Prior probability,Contextual image classification,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
16 | 7 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuemei Zhao | 1 | 111 | 11.89 |
| Lianru Gao | 2 | 373 | 59.90 |
| Zhengchao Chen | 3 | 22 | 10.85 |
| Bing Zhang 0001 | 4 | 22 | 7.16 |
| Wenzhi Liao | 5 | 403 | 31.63 |
| Xuan Yang | 6 | 209 | 15.07 |