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Abstract
Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.
Highlights
Hyperspectral remote sensing combines imaging technology and spectroscopy technology to obtain continuous and narrow-band image data with high spectral resolution [1], which improves the ability to monitor the Earth’s systems and human activities [2,3]
The second experiment took the average accuracy as the evaluation criterion to assess the performance of OP-kernel minimum noise fraction (KMNF) in terms of the support vector machine (SVM) classifier
25% of the samples are randomly selected for training and the rest are employed for testing, and the ten-fold cross-validation is used to find the best parameters in SVM
Summary
Hyperspectral remote sensing combines imaging technology and spectroscopy technology to obtain continuous and narrow-band image data with high spectral resolution [1], which improves the ability to monitor the Earth’s systems and human activities [2,3]. Band selection methods select a subset of spectral features from the original data [9,10]. These methods can be split into six groups [11]: ranking-based methods [12,13,14], search-based methods [15,16], clustering-based methods [17,18,19], sparsity-based methods [20,21,22], embedding-learning-based methods [11], and hybrid-scheme-based methods [23,24,25]. Band selection retains valuable bands for subsequent processing, the algorithms have a large computational burden and often are not robust in complex scenes
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