Author
Listed:
- Wenyi Hu
(College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China)
- Chunjie Lan
(College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China)
- Tian Chen
(College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China)
- Shan Liu
(Department of Modelling, Simulation, and Visualization Engineering, Old Dominion University, Norfolk, VA 23529, USA)
- Lirong Yin
(Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)
- Lei Wang
(Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)
Abstract
Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of researchers’ time, and it is often impossible to find the right model quickly. To address the issue of existing models being too large for easy selection, this paper proposes a multi-path reconfigurable network structure and takes the multi-path reconfigurable residual network (MR-ResNet) model as an example. The reconfigurable neural network model allows researchers to selectively choose the required modules and reassemble them to generate customized models by splitting the trained models and connecting them through modules with different properties. At the same time, by introducing the concept of a multi-path input network, the optimal path is selected by inputting different modules, which shortens the training time of the model and allows researchers to easily find the network model suitable for the current application scenario. A lot of training data, computational resources, and model parameter experience are saved. Three public datasets, NWPU-RESISC45, RSSCN7, and SIRI-WHU datasets, were used for the experiments. The experimental results demonstrate that the proposed model surpasses the classic residual network (ResNet) in terms of both parameters and performance.
Suggested Citation
Wenyi Hu & Chunjie Lan & Tian Chen & Shan Liu & Lirong Yin & Lei Wang, 2024.
"Scene Classification of Remote Sensing Image Based on Multi-Path Reconfigurable Neural Network,"
Land, MDPI, vol. 13(10), pages 1-23, October.
Handle:
RePEc:gam:jlands:v:13:y:2024:i:10:p:1718-:d:1502623
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1718-:d:1502623. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.