Unmanned aerial vehicle image vegetation classification method based on improved DeepLabV3 +

Through data enhancement, feature engineering and model optimization, the UAV image vegetation classification method of DeepLabV3+ was improved, which solved the problems of weak model generalization ability and low computational efficiency in traditional methods and achieved high-precision vegetation classification.

CN120673293APending Publication Date: 2025-09-19SOUTHWEST JIAOTONG UNIV
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Patent Information

Application Number
CN202510840550.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-23
Publication Date
2025-09-19

AI Technical Summary

Technical Problem

Traditional vegetation classification methods based on UAV images using DeepLabV3+ have deficiencies in data processing and model building, resulting in weak model generalization ability, low computational efficiency, and low classification accuracy, making it difficult to meet high-precision classification requirements.

Method used

Python programming is used to implement data enhancement strategies, build a vegetation feature engineering system, replace the Xception backbone network with MobileNetV2, improve the ASPP module and introduce the ECBAM module, combine the SGD optimizer and cosine annealing algorithm to adjust the learning rate, and conduct iterative training and layer-by-layer progressive experiments.

Benefits of technology

The accuracy, robustness and adaptability of vegetation classification in UAV images have been improved, which can meet the vegetation classification needs in complex environments and improve the training efficiency and classification accuracy of the model.

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Abstract

The invention discloses an unmanned aerial vehicle image vegetation classification method based on improved DeepLabV3 +, and the method comprises the steps: preparing an unmanned aerial vehicle image data set, carrying out the Python programming, carrying out the horizontal overturning, vertical overturning and diagonal mirror image expansion of a sample, dividing a training set according to a ratio of 7: 2: 1, cutting an image, constructing a vegetation feature project, extracting texture features through a gray-level co-occurrence matrix, and carrying out the classification of the image vegetation through employing the gray-level co-occurrence matrix. A specific visible light vegetation index is selected, a VDVI index is screened out through a ReliefF algorithm and fused with GEntropy, then a vegetation classification model is constructed, a backbone network is replaced with MobileNetV2, the voidage of an ASPP module is adjusted, an ECBAM is added, features are fused for model training, an SGD optimizer and a cosine annealing algorithm are used for adjusting the learning rate until network convergence is achieved, and finally a test set is used for testing. And carrying out vegetation classification layer-by-layer experiments. The method effectively solves the problem of unmanned aerial vehicle image vegetation classification, and improves the classification precision and model performance through multi-aspect improvement.
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