<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Dynamic features from remote sensing photos may be successfully extracted using deep learning and symmetric network structure, which can then be used to direct them to carry out accurate classification. The DBN model can more effectively extract features from photos since it uses unsupervised learning. It can be reduced to the many symmetric Restricted Boltmann Machines (RBM) training problem. In this paper, a soil rocky desertification (RD) assessment model based on a deep belief network (DBN) is created in light of the complicated influencing aspects of Karst RD risk assessment encompassing several geographical elements. The model builds upon the conventional RBM framework and incorporates the influence layer of related elements as an auxiliary requirement for retrieving Geographic Information System (GIS) score data. Then, in order to forecast the level of soil rocky desertification, it learns the features of many elements. The experimental results show that the proposed model proposed in this paper has better prediction performance and faster convergence speed, and its classification results for different degrees of RD are more consistent with the actual risk assessment results.</p>
</abstract>

Dynamic features from remote sensing photos may be successfully extracted using deep learning and symmetric network structure, which can then be used to direct them to carry out accurate classification. The DBN model can more effectively extract features from photos since it uses unsupervised learning. It can be reduced to the many symmetric Restricted Boltmann Machines (RBM) training problem. In this paper, a soil rocky desertification (RD) assessment model based on a deep belief network (DBN) is created in light of the complicated influencing aspects of Karst RD risk assessment encompassing several geographical elements. The model builds upon the conventional RBM framework and incorporates the influence layer of related elements as an auxiliary requirement for retrieving Geographic Information System (GIS) score data. Then, in order to forecast the level of soil rocky desertification, it learns the features of many elements. The experimental results show that the proposed model proposed in this paper has better prediction performance and faster convergence speed, and its classification results for different degrees of RD are more consistent with the actual risk assessment results.

Evaluation of Soil Rocky Desertification in Karst Region Based on Deep Belief Network

Ecological Chemistry and Engineering S

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Dynamic features from remote sensing photos may be successfully extracted using deep learning and symmetric network structure, which can then be used to direct...

Evaluation of Soil Rocky Desertification in Karst Region Based on Deep Belief Network

Publié en ligne: 08 juil. 2023

Pages: 167 - 173

DOI: https://doi.org/10.2478/eces-2023-0016

Mots clés
Karst region, rocky desertification, DBN, GIS

© 2023 Guan-Yao Lu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Evaluation of Soil Rocky Desertification in Karst Region Based on Deep Belief Network

Publié en ligne: 08 juil. 2023

Pages: 167 - 173

DOI: https://doi.org/10.2478/eces-2023-0016

Mots clésKarst region, rocky desertification, DBN, GIS

© 2023 Guan-Yao Lu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Karst region, rocky desertification, DBN, GIS