Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform

Lyu, Zhiyu; Han, Min; Li, Decai

doi:10.1109/access.2019.2962744

Cited by 3 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, DBN has higher robustness for spectral feature recognition and classification in hyperspectral images, which is consistent with the findings of Maggu et al that the image classification model that is based on DBN has high robustness [ 34 ]. Previous studies on the classification and recognition of remote-sensing hyperspectral images focus mainly on the spectral dimension characteristics of image elements [ 35 ]. However, due to the complexity and the presence of mixed pixels in natural images, it is not sufficient to analyse the spectral characteristics of pixels.…”

Section: Discussionmentioning

confidence: 99%

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

Wang

Gao

et al. 2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%