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Content Provider | IET Digital Library |
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Author | Vaddi, Radhesyam Manoharan, Prabukumar |
Abstract | Hyperspectral image (HSI) consists of hundreds of contiguous spectral bands, which can be used in the classification of different objects on the earth. The inclusion of both spectral as well as spatial features stands essential in order that high classification accuracy is achieved. However, incorporation of the spectral and spatial information without preserving the intrinsic structure of the data leads on to downscaling the classification accuracy. To address the issue aforementioned, the proposed method which involves using unsupervised spectral band selection based on three major constrains: (i) low reconstruction error with neighbourhood bands, (ii) low noise, (iii) high information entropy, is put forward. In addition, the structure-preserving recursive filter is used to extract spatial features. Finally, the classification is performed using convolutional neural networks (CNNs) with different sets of convolutional, pooling, and fully connected layers. To test the performance of the proposed method, experiments have been carried out with three benchmark HSI datasets Indian pines, University of Pavia, and Salinas. These experiments reveal that the proposed method offers better classification accuracy over the purportedly state-of-the-art methods in terms of standard metrics like overall accuracy, average accuracy, and kappa coefficient (K). The proposed method has attained OAs of 99.9, 98.9, and 99.93% for the three datasets, respectively. |
Starting Page | 3909 |
Ending Page | 3919 |
Page Count | 11 |
ISSN | 17519659 |
Volume Number | 14 |
e-ISSN | 17519667 |
Issue Number | Issue 15, Dec (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/14/15 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0728 |
Journal | IET Image Processing |
Publisher Date | 2020-12-04 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Benchmark HSI Datasets Indian Pines Combinatorial Optimisation Computer Vision And Image Processing Technique Contiguous Spectral Band Convolutional Neural Nets Convolutional Neural Network Data And Information; Acquisition, Processing, Storage And Dissemination in Geophysics Entropy Feature Extraction Geography And Cartography Computing Geophysical Image Processing Geophysical Technique And Equipment High Classification Accuracy High Information Entropy Hyperspectral Image Hyperspectral Remote Sensing Image Classification Image Classification Image Recognition Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Intrinsic Structure Low Reconstruction Error Neighbourhood Band Remote Sensing Spatial Feature Spatial Information Spectral Information Structure-preserving Recursive Filter Un-supervised Band Selection Unsupervised Learning Unsupervised Spectral Band Selection |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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