Feature Reduction Based on the Fusion of Spectral and Spatial Transformation for Hyperspectral Image Classification
| Autor: | Md. Moazzem Hossain, Md. Ali Hossain, Md. Mamun Hossain, Md. Al Mamun |
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| Rok vydání: | 2020 |
| Předmět: |
Contextual image classification
Artificial neural network Computer science business.industry Dimensionality reduction Hyperspectral imaging Pattern recognition Convolutional neural network Kernel principal component analysis Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition Principal component analysis Artificial intelligence business |
| Zdroj: | 2020 IEEE Region 10 Symposium (TENSYMP). |
| DOI: | 10.1109/tensymp50017.2020.9230710 |
| Popis: | In recent years, the classification of Hyper Spectral Image (HSI) has posed a big challenge for the analysis of multidimensional property of the image. So it is of utmost importance to reduce the dimension of HSIs. There are several ways to reduce the dimension of hyperspectral images such as Principle Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Kernel Entropy Component Analysis (KECA) and so on. Through this article, We proposed an updated variant of KPCA using multiple kernels such as Linear, RBF, Cosine, Sigmoid, etc. We fused their spectral and special properties by classifying the HSIs using Hybrid Spectral Net Model (HybridSN) which is a recently trending modified deep neural network algorithm using Convolutional Neural Network (CNN). This paper presents empirical outcomes of the effects of using different kernels of KPCA algorithm and their performances regarding the classification of well-known hyperspectral data sets. |
| Databáze: | OpenAIRE |
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