Tree recognition based on HSV-corrosion method and SSD method combining colour complexity and adaptive switching idea. (6th April 2022)

• Record Type: Journal Article
• Authors: Song, Meihui; Wang, Yufeng; Han, Qiaoling; Zhao, Yandong; Zhao, Yue
• Is Part Of: International journal of systems, control and communications. Volume 13:Number 2(2022)
• Page Start: 174
• Page End: 192
• Publication Date: 2022-04-06
• Subjects: adaptive switching -- deep learning -- tree recognition -- single shot multibox detector -- SSD -- colour feature -- HSV colour space; Computer networks -- Periodicals; Intelligent control systems -- Periodicals; Systems engineering -- Periodicals; 004.605
• Languages: English
• ISSNs: 1755-9340

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.