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Tree recognition based on HSV-corrosion method and SSD method combining colour complexity and adaptive switching idea

Tree recognition based on HSV-corrosion method and SSD method combining colour complexity and... The recognition and quantification of trees in forest remote sensing images has become an important task. However, due to the complex background of forest remote sensing images, the accuracy of tree identification is low. To solve this problem, an adaptive switching tree recognition method based on HSV-corrosion method and single shot multibox detector (SSD) method is proposed. Firstly, the switching factor F was determined by the colour complexity. Then, based on the switching factor, forest remote sensing images are divided into two sub-datasets. Finally, HSV-corrosion and SSD methods were combined to adaptively identify trees in two types of forest remote sensing images. The HSV-corrosion method has better recognition performance for dataset 1. In contrast, the SSD method has better recognition performance for dataset 2. Compared with the HSV-corrosion method and SSD method, the proposed method had a higher accuracy (0.86), F1-score values (0.69) and efficiency for all forest remote sensing images. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Systems, Control and Communications Inderscience Publishers

Tree recognition based on HSV-corrosion method and SSD method combining colour complexity and adaptive switching idea

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-9340
eISSN
1755-9359
DOI
10.1504/ijscc.2022.122271
Publisher site
See Article on Publisher Site

Abstract

The recognition and quantification of trees in forest remote sensing images has become an important task. However, due to the complex background of forest remote sensing images, the accuracy of tree identification is low. To solve this problem, an adaptive switching tree recognition method based on HSV-corrosion method and single shot multibox detector (SSD) method is proposed. Firstly, the switching factor F was determined by the colour complexity. Then, based on the switching factor, forest remote sensing images are divided into two sub-datasets. Finally, HSV-corrosion and SSD methods were combined to adaptively identify trees in two types of forest remote sensing images. The HSV-corrosion method has better recognition performance for dataset 1. In contrast, the SSD method has better recognition performance for dataset 2. Compared with the HSV-corrosion method and SSD method, the proposed method had a higher accuracy (0.86), F1-score values (0.69) and efficiency for all forest remote sensing images.

Journal

International Journal of Systems, Control and CommunicationsInderscience Publishers

Published: Jan 1, 2022

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