Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

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.

Nonlinear Noise Reduction for the Airborne Transient Electromagnetic Method based on Kernel Minimum Noise Fraction

The airborne transient electromagnetic method has become a powerful tool to explore deep resource and tectonic structures. However, aircraft vibrations and flight environments produce very strong and complex nonlinear noise and result in poor data quality compared to ground transient electromagnetic methods. Consequently, the reduction of airborne electromagnetic noises is of vital importance to data inversion and imaging. To suppress and remove the nonlinear noise, we propose using kernel minimum noise fraction (KMNF), which is a nonlinear generalized method of minimum noise fraction. First, an adaptive variable window-width filtering algorithm is used to evaluate the noises and perform the preliminary denoising. Then, we adopt the two filter methods, which are minimum noise fraction (MNF) and KMNF to suppress the noise. The results show that these two methods can both suppress noise and make the decay curves smooth, but kernel MNF is more effective for the nonlinear characteristics of noise and it does not weaken the anomaly. Finally, field data from the Qinling mine area is processed, using the MNF and KMNF methods. The results show that nonlinear noise is suppressed by both methods but the results of KMNF are better than those of the linear MNF method.

Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and support vector machines. Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP).