首页|An adaptive superpixelwise coordination factor analysis approach for feature extraction of hyperspectral images
An adaptive superpixelwise coordination factor analysis approach for feature extraction of hyperspectral images
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NETL
NSTL
Taylor & Francis
ABSTRACT Dimensionality reduction of hyperspectral image (HSI) is crucial in improving detector performance. However, employing heterogeneous and homogeneous spatial regions in dimensionality reduction necessitates more comprehensive consideration. Therefore, we propose an adaptive superpixelwise coordination factor analysis (ASCFA) framework for hyperspectral classification. An adaptive denoising strategy is developed using superpixel segmentation based on entropy rates. This approach removes noise from each superpixelwise block by employing an enhanced median filtering technique. It adaptively adjusts the filter window size, effectively preserving edges and fine details while eliminating noise. After the denoising process, a novel unsupervised dimensionality reduction method, grounded in superpixelwise coordination factor analysis, is utilized to estimate the parameters of linear low-dimensional manifolds. These manifolds are then aligned parametric, transforming the denoised HSI into an optimal low-dimensional subspace. The resulting low-dimensional features are not only discriminative and compact but also robust against noise, significantly improving classification performance. To validate the effectiveness of ASCFA, we conducted extensive experiments on three benchmark datasets: Indian Pines, Pavia University, and WHU-Hi-Longkou. ASCFA maintains the highest overall accuracy values on all datasets, even with various added noise. These results underscore the robustness of ASCFA as an effective tool for hyperspectral image analysis, offering improved classification performance and reduced computational complexity.
NETL
NSTL
Taylor & Francis
