Research on computer multi feature fusion SVM model based on remote sensing image recognition and low energy system
Yangming Wu, Hao Wu, Xin Tang, Jianwei Lv, Rufei Zhang
Abstract
• Remote sensing image is widely used in environmental monitoring, land use and other fields. • This paper aims to explore a low-energy multi-feature fusion support vector machine (SVM) model based on remote sensing image recognition. • A series of experiments were carried out on a low-energy hardware platform to test its performance in different scenarios. With the development of remote sensing technology, remote sensing image is widely used in environmental monitoring, land use and other fields. However, traditional image recognition methods often face the problems of high consumption of computing resources and slow processing speed, especially in low-energy systems. Therefore, this paper aims to explore a low-energy multi-feature fusion support vector machine (SVM) model based on remote sensing image recognition. The characteristics of remote sensing images and their application requirements in low energy consumption systems are analyzed, and a multi-feature extraction method combining spectral features, texture features and shape features is proposed. Aiming at the limitations of the traditional SVM model in processing high-dimensional data, an optimization algorithm is designed to reduce the computational complexity and improve the recognition accuracy of the model through dimensionality reduction and feature selection. Based on the model, a series of experiments were carried out on a low-energy hardware platform to test its performance in different scenarios. The experimental results show that the proposed multi-feature fusion SVM model has significantly improved recognition accuracy compared with the single feature method, and can effectively control the computing resource consumption, and can realize real-time recognition on low-energy systems.