Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification
Prime objective of this letter is to select the most relevant features from the original set and perform two steps of feature extraction operations on those selected set, for the classification of hyperspectral remote sensing (HSRS) images. Neighborhood rough sets (NRSs)-based method is used for fea...
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| Published in: | IEEE geoscience and remote sensing letters Vol. 17; no. 3; pp. 489 - 493 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1545-598X, 1558-0571 |
| Online Access: | Get full text |
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| Abstract | Prime objective of this letter is to select the most relevant features from the original set and perform two steps of feature extraction operations on those selected set, for the classification of hyperspectral remote sensing (HSRS) images. Neighborhood rough sets (NRSs)-based method is used for feature selection because of its excellent neighboring information capturing ability. On these selected features, two steps of extraction operations are performed using the stationary wavelet transform (WT) and stacked deep autoencoder (SDAE). Stationary WT extracts the features by exploiting the spectral-spatial information and stacked DAE extracts through representative learning of input information. The wavelet features and the original input spectral features are cascaded to feed as input to the stacks DAE for feature extraction and classification tasks. The proposed classification model with these operational steps possesses the ability to capture more informative features with improved spectral-spatial information that are highly beneficial for the classification of complex data sets, like HSRS images. Simulation results with two HSRS images justified the efficacy of the proposed model compared to other similar methods in terms of different performance measurement indexes. |
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| AbstractList | Prime objective of this letter is to select the most relevant features from the original set and perform two steps of feature extraction operations on those selected set, for the classification of hyperspectral remote sensing (HSRS) images. Neighborhood rough sets (NRSs)-based method is used for feature selection because of its excellent neighboring information capturing ability. On these selected features, two steps of extraction operations are performed using the stationary wavelet transform (WT) and stacked deep autoencoder (SDAE). Stationary WT extracts the features by exploiting the spectral–spatial information and stacked DAE extracts through representative learning of input information. The wavelet features and the original input spectral features are cascaded to feed as input to the stacks DAE for feature extraction and classification tasks. The proposed classification model with these operational steps possesses the ability to capture more informative features with improved spectral–spatial information that are highly beneficial for the classification of complex data sets, like HSRS images. Simulation results with two HSRS images justified the efficacy of the proposed model compared to other similar methods in terms of different performance measurement indexes. |
| Author | Meher, Saroj K. |
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| SubjectTerms | Classification Computer simulation Deep autoencoder (DAE) Feature extraction Hyperspectral imaging Image classification land use/covers classification Performance indices Performance measurement Principal component analysis Remote sensing remote sensing (RS) image Rough sets Spatial data Spectra Task analysis wavelet transform (WT) Wavelet transforms |
| Title | Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification |
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