|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'500'096
Articles rated: 2609
19 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
The global information for land cover classification by dual-branch deep learning | | Fan Zhang
; MinChao Yan
; Chen Hu
; Jun Ni
; Fei Ma
; | | Date: |
30 May 2020 | | Abstract: | Land cover classification has played an important role in remote sensing
because it can intelligently identify things in one huge remote sensing image
so as to reduce the work of human. However, a lot of classification methods are
designed based on the pixel feature or limited spatial feature of the remote
sensing image, which limits the classification accuracy and universality of
their methods. This paper proposed a novel method to take into the information
of remote sensing image, i.e. geographic latitude-longitude information. In
addition, a dual-channel convolutional neural network (CNN) classification
method is designed to mine pixel feature of image in combination with the
global information simultaneously. Firstly, 1-demensional network of CNN is
designed to extract pixel information of remote sensing image, and the fully
connected network (FCN) is employed to extract latitude-longitude feature.
Then, their features of two neural networks are fused by another fully neural
network to realize remote sensing image classification. Finally, two kinds of
remote sensing, involving hyperspectral imaging (HSI) and polarimetric
synthetic aperture radar (PolSAR), are used to verify the effectiveness of our
method. The results of the proposed method is superior to the traditional
single-channel convolutional neural network. | | Source: | arXiv, 2006.0234 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER