高光谱空谱一体化图像分类研究

高光谱图像分类是遥感图像处理技术中的一个热点,提高分类精度是目前一个重要研究方向。常规的高光谱图像分类技术主要关注于如何更好地利用光谱空间的分类信息,往往忽视图像空间域信息。本文提出了一种基于空谱一体化处理的高光谱图像分类方法,在利用数据进行自身光谱特征分类的同时采用区域生长法和二值形态学法相结合的空间域有效信息对光谱分类结果进行补充。实验证明本方法能提高高光谱图像分类精度。     

Image completion with approximate convex hull tensor decomposition

Many image completion methods are based on a low-rank approximation of the underlying image using matrix or tensor decomposition models. In this study, we assume that the image to be completed is represented by a multi-way array and can be approximated by a conical hull of subtensors in the observation space. If an observed tensor is near-separable along at least one mode, the extreme rays, represented by the selected subtensors, can be found by analyzing the corresponding convex hull. Following this assumption, we propose a geometric algorithm to address a low-rank image completion problem. The extreme rays are extracted with a segmented convex-hull algorithm that is suitable for performing noise-resistant non-negative tensor factorization. The coefficients of a conical combination of such rays are estimated using Douglas–Rachford splitting combined with the rank-two update least-squares algorithm. The proposed algorithm was applied to incomplete RGB images and a hyperspectral 3D array with a large number of randomly missing entries. Experiments confirm its good performance with respect to other well-known image completion methods.     

Kernel-based methods for hyperspectral image classification

This paper presents the framework of kernel-based methods in the context of hyperspectral image classification, illustrating from a general viewpoint the main characteristics of different kernel-based approaches and analyzing their properties in the hyperspectral domain. In particular, we assess performance of regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVMs), kernel Fisher discriminant (KFD) analysis, and regularized AdaBoost (Reg-AB). The novelty of this work consists in: 1) introducing Reg-RBFNN and Reg-AB for hyperspectral image classification; 2) comparing kernel-based methods by taking into account the peculiarities of hyperspectral images; and 3) clarifying their theoretical relationships. To these purposes, we focus on the accuracy of methods when working in noisy environments, high input dimension, and limited training sets. In addition, some other important issues are discussed, such as the sparsity of the solutions, the computational burden, and the capability of the methods to provide outputs that can be directly interpreted as probabilities.     

Perceptual-based image fusion for hyperspectral data

Three hierarchical multiresolution image fusion techniques are implemented and tested using image data from the Airborne Visual/Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The methods presented focus on combining multiple images from the AVIRIS sensor into a smaller subset of images white maintaining the visual information necessary for human analysis. Two of the techniques are published algorithms that were originally designed to combine images from multiple sensors, but are shown to work well on multiple images from the same sensor. The third method presented was developed specifically to fuse hyperspectral images for visual analysis. This new method uses the spatial frequency response (contrast sensitivity) of the human visual system to determine which features in the input images need to be preserved in the composite image(s) thus ensuring the composite image maintains the visually relevant features from each input image. The image fusion algorithms are analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor. After analyzing the signal-to-noise ratios and visual aesthetics of the fused images, contrast sensitivity based fusion is shown to provide excellent fusion results and, in every case, outperformed the other two methods     

Signal processing for hyperspectral image exploitation

Electro-optical remote sensing involves the acquisition of information about an object or scene without coming into physical contact with it. This is achieved by exploiting the fact that the materials comprising the various objects in a scene reflect, absorb, and emit electromagnetic radiation in ways characteristic of their molecular composition and shape. If the radiation arriving at the sensor is measured at each wavelength over a sufficiently broad spectral band, the resulting spectral signature, or simply spectrum, can be used (in principle) to uniquely characterize and identify any given material. An important function of hyperspectral signal processing is to eliminate the redundancy in the spectral and spatial sample data while preserving the high-quality features needed for detection, discrimination, and classification. This dimensionality reduction is implemented in a scene-dependent (adaptive) manner and may be implemented as a distinct step in the processing or as an integral part of the overall algorithm. The most widely used algorithm for dimensionality reduction is principal component analysis (PCA) or, equivalently, Karhunen-Loeve transformation     

Prototype image constraints for set-theoretic image restoration

Constraints based on prototype images are developed and used in set-theoretic image restoration. A prototype can be obtained as a result of applying a predetermined operator to the observed image. In this case, the operator and the bound, which limits the variation of the restored image from the prototype, are the two defining quantities of a prototype constraint. General guidelines for rigorously estimating the defining bound of a prototype constraint under certain simplifying conditions are discussed. The authors provide two examples of prototype constraints where the prototypes are obtained by the Wiener filtering operator and a local averaging operator. The projection onto convex sets algorithm using the prototype constraints is applied to both monochrome and color images degraded by out-of-focus blur at different noise levels. The results show significant improvement over the Wiener restoration in reducing the restoration artifacts     

Unsupervised hyperspectral image analysis with projection pursuit

Principal components analysis (PCA) is effective at compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of data variance. Unfortunately, information content in hyperspectral images does not always coincide with such projections. The authors propose an application of projection pursuit (PP), which seeks to find a set of projections that are "interesting," in the sense that they deviate from the Gaussian distribution assumption. Once these projections are obtained, they can be used for image compression, segmentation, or enhancement for visual analysis. To find these projections, a two-step iterative process is followed where they first search for a projection that maximizes a projection index based on the information divergence of the projection's estimated probability distribution from the Gaussian distribution and then reduce the rank by projecting the data onto the subspace orthogonal to the previous projections. To calculate each projection, they use a simplified approach to maximizing the projection index, which does not require an optimization algorithm. It searches for a solution by obtaining a set of candidate projections from the data and choosing the one with the highest projection index. The effectiveness of this method is demonstrated through simulated examples as well as data from the hyperspectral digital imagery collection experiment (HYDICE) and the spatially enhanced broadband array spectrograph system (SEBASS).     

Multispectral image restoration with multisensors

A procedure is advanced to restore a single color image, which has been degraded by a linear shift-invariant blur in the presence of additive stationary noise. Four sensors are needed, followed by the application of the RGB-to-YIQ transformation. Subsequently, one three-dimensional (3D) Wiener filter on a sequence of two luminance component images and two two-dimensional (2D) Wiener filters on each of the two chrominance component images are needed. For the procedure to be successful, the imposition of a strongly coprime condition on the wavenumber response of two distinct sensor blur functions is necessary. The resulting well-conditioned problem is shown to provide improved restoration over the decorrelated component and the independent channel restoration methods, each of which uses one sensor for each of the three primary color components     

Robust hyperspectral image coding with channel-optimizedtrellis-coded quantization

This paper presents a wavelet-based hyperspectral image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. This optimization is performed only at the level of the source encoder and does not include any channel coding for error protection. The robust nature of the coder increases the security level of the encoded bit stream, and provides a much higher quality decoded image. In the absence of channel noise, the proposed coder is shown to achieve a compression ratio greater than 70:1, with an average peak SNR of the coded hyperspectral sequence exceeding 40 dB. Additionally, the coder is shown to exhibit graceful degradation with increasing channel errors     

ENAS-RIF图像复原算法

大气湍流严重影响天文、遥感等光学观测的成像效果,必须进行图像复原处理后才能获取更清晰的图像.为了提高图像复原效果,提出了一种基于可靠支持域和改进代价函数的增强型非负性和有限支撑域的递归逆滤波器(ENAS-RIF)图像复原算法.首先,利用Curvelet去噪进行图像平滑的预处理,抑制图像噪声;然后利用图像阈值分割和形态学...     

Representative band selection for hyperspectral image classification

High dimensional curse for hyperspectral images is one major challenge in image classification. In this work, we introduce a novel spectral band selection method by representative band mining. In the proposed method, the distance between two spectral bands is measured by using disjoint information. For band selection, all spectral bands are first grouped into clusters, and representative bands are selected from these clusters. Different from existing clustering-based band selection methods which select bands from each cluster individually, the proposed method aims to select representative bands simultaneously by exploring the relationship among all band clusters. The optimal representative band selection is based on the criteria of minimizing the distance inside each cluster and maximizing the distance among different representative bands. These selected bands can be further applied in hyperspectral image classification. Experiments are conducted on the 92AV3C Indian Pine data set. Experimental results show that the disjoint information-based spectral band distance measure is effective and the proposed representative band selection approach outperforms state-of-the-art methods for high dimensional image classification.     

Hessian-based norm regularization for image restoration with biomedical applications

We present nonquadratic Hessian-based regularization methods that can be effectively used for image restoration problems in a variational framework. Motivated by the great success of the total-variation (TV) functional, we extend it to also include second-order differential operators. Specifically, we derive second-order regularizers that involve matrix norms of the Hessian operator. The definition of these functionals is based on an alternative interpretation of TV that relies on mixed norms of directional derivatives. We show that the resulting regularizers retain some of the most favorable properties of TV, i.e., convexity, homogeneity, rotation, and translation invariance, while dealing effectively with the staircase effect. We further develop an efficient minimization scheme for the corresponding objective functions. The proposed algorithm is of the iteratively reweighted least-square type and results from a majorization-minimization approach. It relies on a problem-specific preconditioned conjugate gradient method, which makes the overall minimization scheme very attractive since it can be applied effectively to large images in a reasonable computational time. We validate the overall proposed regularization framework through deblurring experiments under additive Gaussian noise on standard and biomedical images.     

有监督的高光谱图像伪装目标检测方法

针对伪装目标检测问题,提出了一种有监督的高光谱伪装目标检测方法。以植被型伪装目标为研究对象,在分析伪装材料与绿色植被光谱之间特性的基础上,先通过光谱重排、光谱微分以及光谱差异性增强处理,对植被型伪装材料与真实植被（背景）之间的光谱差异进行放大,然后利用主成分分析（PCA）变换进行降维,从而实现了一种适用于大面积植被型伪装目标的高光谱检测方法。实验结果表明,该检测方法在检测时间和检测效果上要优于基于加权的约束能量最小化法（WCM-CEM）和基于非监督目标生成处理的正交子空间投影法（UTGP-OSP）。     

Generative adversarial networks for hyperspectral image spatial super-resolution

It is becoming increasingly easier to obtain more abundant supplies for hyperspectral images ( HSIs). Despite this, achieving high resolution is still critical. In this paper, a method named hyperspectral images super-resolution generative adversarial network ( HSI-RGAN ) is proposed to enhance the spatial resolution of HSI without decreasing its spectral resolution. Different from existing methods with the same purpose, which are based on convolutional neural networks ( CNNs) and driven by a pixel-level loss function, the new generative adversarial network (GAN) has a redesigned framework and a targeted loss function. Specifically, the discriminator uses the structure of the relativistic discriminator, which provides feedback on how much the generated HSI looks like the ground truth. The generator achieves more authentic details and textures by removing the place of the pooling layer and the batch normalization layer and presenting smaller filter size and two-step upsampling layers. Furthermore, the loss function is improved to specially take spectral distinctions into account to avoid artifacts and minimize potential spectral distortion, which may be introduced by neural networks. Furthermore, pre-training with the visual geometry group (VGG) network helps the entire model to initialize more easily. Benefiting from these changes, the proposed method obtains significant advantages compared to the original GAN. Experimental results also reveal that the proposed method performs better than several state-of-the-art methods.     

Raised cosine function for image restoration

In this paper, a new set of raised cosine functions is proposed. These functions have all the useful properties of the spline functions with the additional advantage of continuous infinite derivatives as against only a finite number of derivatives in case of the spline functions. Because of this property they exhibit a smoother behaviour. The property of smoothness, coupled with convolution, makes the raised cosine functions readily applicable to the image restoragtion problem, where the degradation is through a shift invariant blurring function. The results confirm the superior behaviour of these functions in comparison to spline functions.     

Sparse representation for color image restoration

Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.     

Learning-based image restoration for compressed images

In this paper, we propose a novel learning-based image restoration scheme for compressed images by suppressing compression artifacts and recovering high frequency (HF) components based upon the priors learnt from a training set of natural images. The JPEG compression process is simulated by a degradation model, represented by the signal attenuation and the Gaussian noise addition process. Based on the degradation model, the input image is locally filtered to remove Gaussian noise. Subsequently, the learning-based restoration algorithm reproduces the HF component to handle the attenuation process. Specifically, a Markov-chain based mapping strategy is employed to generate the HF primitives based on the learnt codebook. Finally, a quantization constraint algorithm regularizes the reconstructed image coefficients within a reasonable range, to prevent possible over-smoothing and thus ameliorate the image quality. Experimental results have demonstrated that the proposed scheme can reproduce higher quality images in terms of both objective and subjective quality.     

Iterative Wiener filters for image restoration

The iterative Wiener filter, which successively uses the Wiener-filtered signal as an improved prototype to update the covariance estimates, is investigated. The convergence properties of this iterative filter are analyzed. It has been shown that this iterative process converges to a signal which does not correspond to the minimum mean-squared-error solution. Based on the analysis, an alternate iterative filter is proposed to correct for the convergence error. The theoretical performance of the filter has been shown to give minimum mean-squared error. In practical implementation when there is unavoidable error in the covariance computation, the filter may still result in undesirable restoration. Its performance has been investigated and a number of experiments in a practical setting were conducted to demonstrate its effectiveness