The integration of spatial context in the classification of hyperspectral images is known to be an effective way in improving classification accuracy. In this paper, a novel spectral-spatial classification framework based on edge-preserving filtering is proposed. The proposed framework consists of the following three steps. First, the hyperspectral image is classified using a pixelwise classifier, e.g., the support vector machine classifier. Then, the resulting classification map is represented as multiple probability maps, and edge-preserving filtering is conducted on each probability map, with the first principal component or the first three principal components of the hyperspectral image serving as the gray or color guidance image. Finally, according to the filtered probability maps, the class of each pixel is selected based on the maximum probability. Experimental results demonstrate that the proposed edge-preserving filtering based classification method can improve the classification accuracy significantly in a very short time. Thus, it can be easily applied in real applications.

Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering

Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency.

Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering

http://www.math.hkbu.edu.hk/ICM/pdf/10-05.pdf

Restoration of images corrupted by mixed Gaussian-impulse noise via l1-l0 minimization

Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality.

/pdf/mixed-noise-removal-by-weighted-encoding-with-sparse-3xpo2owdqh.pdf

Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization

Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.

A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means

In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allocate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter inside the bilateral filter switches between the Gaussian and impulse modes depending upon the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and-pepper with Gaussian noise. In addition, the computational complexity of SBF is significantly less than that of other mixed noise filters.

Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal

In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allocate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter inside the bilateral filter switches between the Gaussian and impulse modes depending on the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and-pepper with Gaussian noise. In addition, the computational complexity of SBF is significantly less than that of other mixed noise filters.

Chih Hsing Lin

Papers

Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal

Switching bilateral filter with a texture/noise detector for universal noise removal