Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

Abstract

To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.

Glimpse into Botswana’s AI Readiness Landscape

“…Nevertheless, all hope is not lost as several AI studies indicated that they used Botswana datasets in various areas. For instance; Hyperspectral images, Orapa diamond mine provided blasting datasets, and there is data available in the United Nations SDG data repository (Gaopale, et al, 2019;Li, et al, 2020;Mwitondi, et al, 2020;Seera & Lim, 2014).…”

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm

“…For example, disease detection (Jin et al, 2013), weed detection (Papp et al, 2021), and terrain classification (Li et al, 2020b). And because hyperspectral images contain a large amount of information and have a sizeable sparse data structure, researchers often use SVM methods as the primary modeling tool.…”

A Bibliometric Analysis: Remote Sensing Literature in Dimensions.ai Database

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