Name
Title | ||
|---|---|---|
Complex Scene Classification of High Resolution Remote Sensing Images Based on DCNN Model |
Abstract | ||
|---|---|---|
Aiming at the problem that the traditional scene classification methods are not accurate to the semantic description of high resolution remote sensing images, a method based on deep convolutional neural network (DCNN) is proposed. It achieves an accuracy of 93% on the dataset (whu-6) made by myself, which significantly improves the classification accuracy compared with the traditional scene classification methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/Multi-Temp.2019.8866895 | 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) |
Keywords | Field | DocType |
high resolution remote sensing image,scene classification,deep convolutional neural network | Kernel (linear algebra),Convolutional neural network,Convolution,Computer science,Remote sensing,Image resolution | Conference |
ISBN | Citations | PageRank |
978-1-7281-4616-4 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dexi Chen | 1 | 0 | 0.34 |
| Peng Hu | 2 | 38 | 12.24 |
| Xuelin Duan | 3 | 0 | 0.68 |