Tuesday, December 31, 2019

Example Of Hyperpectral Image Classification - 1730 Words

This work is mainly related to hyperspectral image classification, with special emphasis on high-dimensional feature vectors. Various techniques and frameworks have been developed to tackle the HSI classification problem. Some of the recent HSI classification techniques can be found in [Yi Chen, Nasser M. Nasrabadi] [12]–[17]. In here, we just emphasize the most recent prominent technique in HSI. A. Dimensionality Reduction With regard to the issue that we are following, there are another popular examples based on dimensional reduction studies, Principal Component Analysis (PCA), Random Projection (RP) that can project the data matrix into another space which is lower dimensional rather than original space [18]. Structurally, in these†¦show more content†¦However, since CNNs have been mostly considered on image and visual-related problems, there are a few prominent works for HSI classification based on deep learning. Chen et al. [36] used deep belief network (DBN) to extract spectral-spatial features for the HSI classification. Yuan et al. [37] applied the CNN model which proposed by Dong et al. [20] on the hyperspectral images. In their work, they didn’t consider preserving the spectral information, and they treat hyperspectral images as a RGB images. Hu et al. [30] presented a new CNN architecture that comprises of an input layer, a convolutional layer, a maxpooling layer, a fully-conn ected layer, and finally an output for hyperspectral image classification. They proposed CNN to directly classify hyperspectral data in the spectral domain. Makantasis et al. [31] presented a deep learning based classification method that hierarchically constructs high level features automatically. Wu et al. [22] developed a novel

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