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.

中文翻译：

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高光谱遥感影像智能地形分类中机器学习的应用。

为了克服在遥感高光谱图像中对地面特征进行自动化和智能分类的难题，将机器学习方法逐步引入到遥感成像过程中。首先，选择PaviaU，博茨瓦纳和Cuprite高光谱数据集作为研究对象，其目的是通过机器学习处理遥感高光谱图像，以实现特征的自动和智能分类。然后，介绍了支持向量机（SVM）和极限学习机（ELM）分类算法的基本原理，并将其应用于数据集。接下来，通过使用受限的玻尔兹曼机（RBM）调整参数估算值，建立了一个基于深度信念网络（DBN）的高光谱图像地形分类模型。接下来，针对准确性和一致性，分析和比较了用于高光谱图像地形分类的SVM，ELM和DBN分类算法。结果表明，三个数据集上ELM的平均检测准确度分别为89.54％，96.14％和96.28％，Kappa系数分别为0.832、0.963和0.924。SVM的平均检测准确度为88.90％，92.11％和91.68％，Kappa系数值为0.768、0.913和0.944；DBN分类模型的平均检测准确度为92.36％，97.31％和98.84％，Kappa系数值为0.883、0.944和0.972。结果还表明，DBN算法的分类精度超过了前两种方法，因为它充分利用了高光谱遥感图像的空间和光谱信息。综上所述，本研究提出的DBN算法在遥感高光谱图像的目标分类中具有较高的应用价值。