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Intelligent classification model of land resource use using deep learning in remote sensing images

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  • Liao, Qingtao

Abstract

Accurate and effective remote sensing image classification and analysis methods can provide reliable support for the sustainable development of land resources, but the current methods can't support the precise processing of complex images. To solve this problem, this paper proposes a new classification and analysis method of remote sensing image land resources based on deep learning. Firstly, this method first uses ENVI software to realize image preprocessing on the collected data set, and realizes reliable data set; At the same time, based on the sparse auto-encoder network model, the sample features of the image data set are deeply mined to make a better reference for category determination; And based on ELM classifier network, convolution layer and sub sampling layer are alternately connected to extract the depth feature of remote sensing image, which can greatly reduce the number of network parameters and improve the computational. Finally, the actual collected data set is used for simulation verification, and the experimental results show that the accuracy of complex image analysis reaches 98.15%, which shows excellent performance of image feature classification and analysis.

Suggested Citation

  • Liao, Qingtao, 2023. "Intelligent classification model of land resource use using deep learning in remote sensing images," Ecological Modelling, Elsevier, vol. 475(C).
    • Handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022003295
    DOI: 10.1016/j.ecolmodel.2022.110231
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    References listed on IDEAS

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    1. Majid Mirzaei & Haoxuan Yu & Adnan Dehghani & Hadi Galavi & Vahid Shokri & Sahar Mohsenzadeh Karimi & Mehdi Sookhak, 2021. "A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation," Sustainability, MDPI, vol. 13(23), pages 1-16, December.
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