Hyperspectral images classification via weighted spatial-spectral dimensionality reduction principle component analysis

doi:10.6046/gtzyyg.2019.02.03

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Abstract

In order to improve the hyperspectral images (HSI) classification accuracy and preprocess HSI by fully using the spatial and spectral information, this paper proposes a new spatial-spectral dimensionality reduction method, i.e., weighted spatial and spectral principle component analysis (WSSPCA). This algorithm reconstructs the HSI by using the physical characteristics of HSI, which can lower the influence of singular point in HSI. Principle component analysis (PCA) is utilized to reduce dimensionality of HIS, and it reduces the redundancy between bands and improves the HSI classification accuracy efficiently. The benchmark tests on PaviaU and Indian Pines demonstrate that the performance of WSSPCA is better than that of PCA and LPP when 5% and 10% samples in each class (10 samples are chosen when the total samples in every class is less than 100) are chosen randomly as train samples. The best values of Kappa coefficient obtained by WSSPCA are 0.955 9 and 0.896 1 respectively on the HSI datasets, exceeding the baseline by 0.193 8 and 0.205 0.

Keywords hyperspectral images classification weighted spatial and spectral principle component analysis dimensionality reduction

TP751

Issue Date: 23 May 2019

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	Ruhan A
	Fang HE
	Biaobiao WANG

Cite this article:

Ruhan A,Fang HE,Biaobiao WANG. Hyperspectral images classification via weighted spatial-spectral dimensionality reduction principle component analysis[J]. Remote Sensing for Land & Resources, 2019, 31(2): 17-23.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.03 OR https://www.gtzyyg.com/EN/Y2019/V31/I2/17

Fig.1 Adjacent space of a pixel in different cases

Fig.2 Images and types of objects of PaviaU and Indian Pines

Fig.3 Relationship between different parameters and classification accuracy on PaviaU dataset

Fig.4 Relationship between dimension and classification accuracy on PaviaU dataset

Tab.1 Best result and corresponding dimension on PaviaU dataset

Fig.5 Classification results

Fig.6 Relationship between different parameters and classification accuracy on Indian Pines dataset

Fig.7 Relationship between dimension and classification accuracy on Indian Pines dataset

Tab.2 Best result and corresponding dimension on Indian Pines dataset

Fig.8 Classification results

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