High Performance Hyperspectral Image Classification using Graphics Processing Units

Hossam, Mahmoud

doi:10.48550/arxiv.2106.12942

Cited by 1 publication

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“…There are three research topics for the classification method of HSIs: clustering based on graph theory [ 16 , 17 ], clustering by using a machine-learning algorithm [ 18 , 19 ], and clustering with the hybrid kernels [ 21 , 22 , 23 , 24 ]. The HCW-SSC represents the clustering with the hybrid kernels.…”

Section: Discussionmentioning

confidence: 99%

“…Since HSIs usually have a high spectral and also spatial resolution and the aim of classification of an HSI is to precisely map, monitor, and detect valuable land cover/use change, the classification method of HSIs is different from the classification method for the multispectral image. There are three research topics for the classification method of HSIs: clustering based on graph theory [ 16 , 17 ], clustering by using a machine-learning algorithm [ 18 , 19 , 20 ], and clustering with the hybrid kernels [ 21 , 22 , 23 , 24 ] The clustering based on the graph theory heavily depends on auxiliary space to hold the cache, which needs the graphics processing unit (GPU) and extra random-access memory (RAM). The clustering by using a machine-learning algorithm usually has nonlinear time complexity.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Clustering Method with a Filter Feature Selection for Hyperspectral Image Classification

Zhang

2022

J. Imaging

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Hyperspectral images (HSI) provide ample spectral information of land cover. The hybrid classification method works well for HSI; however, how to select the suitable similarity measures as kernels with the appropriate weights of hybrid classification for HSI is still under investigation. In this paper, a filter feature selection was designed to select the most representative features based on similarity measures. Then, the weights of applicable similarity measures were computed based on coefficients of variation (CVs) of similarity measures. Implementing the similarity measures as the kernels with weights into the K-means algorithm, a new hybrid changing-weight classification method with a filter feature selection (HCW-SSC) was developed. Standard spectral libraries, operative modular imaging spectrometer (OMIS) airborne HSI, airborne visible/infrared imaging spectrometer (AVIRIS) HSI, and Hyperion satellite HSI were selected to inspect the HCW-SSC method. The results showed that the HCW-SSC method has the highest overall accuracy and kappa coefficient (or F1 score) in all experiments (97.5% and 0.974 for standard spectral libraries, 93.21% and 0.9245 for OMIS, 79.24% and 0.8044 for AVIRIS, and 81.23% and 0.7234 for Hyperion) compared to the classification methods (93.75% and 0.958 for standard spectral libraries, 88.27% and 0.8698 for OMIS, 73.12% and 0.7225 for AVIRIS, and 56.34% and 0.3623 for Hyperion) without feature selection and the machine-learning method (68.27% and 0.6628 for AVIRIS, and 51.21% and 0.4255 for Hyperion). The experimental results demonstrate that the new hybrid method performs more effectively than the traditional hybrid method. This also shed a light on the importance of feature selection in HSI classification.

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