Abstract
Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.
Highlights
With the development of imaging instruments in the past few years, hyperspectral data processing has become increasingly more important in many fields [1,2,3,4,5]
It should be emphasized that the features containing more than 99% of cumulative eigenvalues were selected when discriminant analysis feature extraction (DAFE) and decision boundary feature extraction (DBFE) were employed in the following experiments
In the Indian Pines dataset, the results indicate that the AUTOMATIC approach is affected by different feature extraction (FE) methods
Summary
With the development of imaging instruments in the past few years, hyperspectral data processing has become increasingly more important in many fields [1,2,3,4,5]. As a data tool with high spectral resolution, hyperspectral sensors usually utilized hundreds of spectral channels to describe spectral signatures. The primary purpose of hyperspectral images (HSI) processing is to analyze and recognize spectral data acquired by hyperspectral sensors. Different materials have distinct reflectance spectral signatures. Reflectance spectra are always used for material recognition and image analysis [6]. While the high dimensionality of HSI supports accurate descriptions for spectral signatures, they lead to some theoretical and practical problems, the curse of dimensionality problem
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