Biao Hou

SAR Image Despeckling Based on Local Homogeneous-Region Segmentation by Using Pixel-Relativity Measurement

This paper provides a novel pointwise-adaptive speckle filter based on local homogeneous-region segmentation with pixel-relativity measurement. A ratio distance is proposed to measure the distance between two speckled-image patches. The theoretical proofs indicate that the ratio distance is valid for multiplicative speckle, while the traditional Euclidean distance failed in this case. The probability density function of the ratio distance is deduced to map the distance into a relativity value. This new relativity-measurement method is free of parameter setting and more functional compared with the Gaussian kernel-projection-based ones. The new measurement method is successfully applied to segment a local shape-adaptive homogeneous region for each pixel, and a simplified strategy for the segmentation implementation is given in this paper. After segmentation, the maximum likelihood rule is introduced to estimate the true signal within every homogeneous region. A novel evaluation metric of edge-preservation degree based on ratio of average is also provided for more precise quantitative assessment. The visual and numerical experimental results show that the proposed filter outperforms the existing state-of-the-art despeckling filters.

SAR Image Despeckling Based on Nonsubsampled Shearlet Transform

Synthetic Aperture Radar (SAR) Image despeckling is an important problem in SAR image processing since speckle may interfere with automatic interpretation. This paper presents a new approach for despeckling based on nonsubsampled shearlet transform. The approach introduced here presents two major contributions: (a) Translation-invariant Nonsubsampled Shearlet Transform (NSST) is designed to get more directional subbands which help to capture the anisotropic information of SAR image, and an estimation of speckle variance based on NSST is modeled to shrink NSST coefficients; (b) NSST coefficients are divided into several classes based on NSST- Multiscale Local Coefficient Variation (NSST-MLCV), which is helpful to reduce the undesired over-shrinkage, and shrinkage factor is obtained by computing the prior ratio and the likelihood ratio through mask. This model allows us to classify the NSST coefficients into classes having different degrees of heterogeneity, which can reduce the shrinkage ratio for heterogeneity regions while suppresses speckle effectively to realize both despeckling and detail preservation. Experimental results, carried out on both artificially speckled images and true SAR images, demonstrate that the proposed filtering approach outperforms the previous filters, irrespective of the features of the underlying reflectivity.

Natural and Remote Sensing Image Segmentation Using Memetic Computing

In order to solve the image segmentation problem which assigns a label to every pixel in an image such that pixels with the same label share certain visual characteristics more effectively, a novel approach based on memetic algorithm (MISA) is proposed. Watershed segmentation is applied to segment original images into non-overlap small regions before performing the portioning process by MISA. MISA adopts a straightforward representation method to find the optimal combination of watershed regions under the criteria of interclass variance in feature space. After implementing cluster-based crossover and mutation, an individual learning procedure moves exocentric regions in current cluster to the one they should belong to according to the distance between these regions and cluster centers in feature space. In order to evaluate the new algorithm, six texture images, three remote sensing images and three natural images are employed in experiments. The experimental results show that MISA outperforms its genetic version, the Fuzzy c-means algorithm, and K-means algorithm in partitioning most of the test problems, and is an effective approach when compared with two state-ofthe-art image segmentation algorithms including an efficient graph-based algorithm and a spectral clustering ensemble-based algorithm.

SAR Image Despeckling Using Edge Detection and Feature Clustering in Bandelet Domain

To effectively preserve the edges of a synthetic aperture radar (SAR) image when despeckling, an algorithm with edge detection and fuzzy clustering in the translation-invariant second-generation bandelet transform (TIBT) domain is proposed in this letter. A Canny operator is first utilized to detect and remove edges from the SAR image. Then, TIBT and fuzzy C-mean clustering are employed to decompose and despeckle the edge-removed image, respectively. Finally, the removed edges are added to the reconstructed image. The algorithm suggests each coefficient in high-frequency subbands as the clustering feature, proposes a calculation method of the best clustering number, and defines the signal and noise in the clustering results. Experimental results show that the visual quality and evaluation indexes outperform the other methods with no edge preservation. The proposed algorithm effectively realizes both despeckling and edge preservation and reaches the state-of-the-art performance.

Multivariate Compressive Sensing for Image Reconstruction in the Wavelet Domain: Using Scale Mixture Models

Most wavelet-based reconstruction methods of compressive sensing (CS) are developed under the independence assumption of the wavelet coefficients. However, the wavelet coefficients of images have significant statistical dependencies. Lots of multivariate prior models for the wavelet coefficients of images have been proposed and successfully applied to the image estimation problems. In this paper, the statistical structures of the wavelet coefficients are considered for CS reconstruction of images that are sparse or compressive in wavelet domain. A multivariate pursuit algorithm (MPA) based on the multivariate models is developed. Several multivariate scale mixture models are used as the prior distributions of MPA. Our method reconstructs the images by means of modeling the statistical dependencies of the wavelet coefficients in a neighborhood. The proposed algorithm based on these scale mixture models provides superior performance compared with many state-of-the-art compressive sensing reconstruction algorithms.

Using Combined Difference Image and

In this letter, a simple and effective unsupervised approach based on the combined difference image and k-means clustering is proposed for the synthetic aperture radar (SAR) image change detection task. First, we use one of the most popular denoising methods, the probabilistic-patch-based algorithm, for speckle noise reduction of the two multitemporal SAR images, and the subtraction operator and the log ratio operator are applied to generate two kinds of simple change maps. Then, the mean filter and the median filter are used to the two change maps, respectively, where the mean filter focuses on making the change map smooth and the local area consistent, and the median filter is used to preserve the edge information. Second, a simple combination framework which uses the maps obtained by the mean filter and the median filter is proposed to generate a better change map. Finally, the k-means clustering algorithm with k = 2 is used to cluster it into two classes, changed area and unchanged area. Local consistency and edge information of the difference image are considered in this method. Experimental results obtained on four real SAR image data sets confirm the effectiveness of the proposed approach.

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Multitemporal synthetic aperture radar (SAR) images have been successfully used for the detection of different types of terrain changes. SAR image change detection has recently become a challenge problem due to the existence of speckle and the complex mixture of terrain environment. This paper presents a novel unsupervised change detection method in SAR images based on image fusion strategy and compressed projection. First, a Gauss-log ratio operator is proposed to generate a difference image. In order to obtain a better difference map, image fusion strategy is applied using complementary information from Gauss-log ratio and log-ratio difference image. Second, nonsubsampled contourlet transform (NSCT) is used to reduce the noise of the fused difference image, and compressed projection is employed to extract feature for each pixel. The final change detection map is obtained by partitioning the feature vectors into “changed” and “unchanged” classes using simple k-means clustering. Experiment results show that the proposed method is effective for SAR image change detection in terms of shape preservation of the detected change portion and the numerical results.

-Means Clustering for SAR Image Change Detection

The accurate classification of hyperspectral images is an important task for many practical applications. In this paper, a new method for hyperspectral image classification is proposed based on manifold learning algorithm, The approach introduced here presents three major contributions: 1) a new Laplacian eigenmap pixels distribution-flow (LE PD-Flow) is proposed for hyperspectral image analysis, in which, a new joint spatial-pixel characteristics distance (JSPCD) measure is constructed to improve the accuracy of classification and a suitable weighting factor is used to distinguish data points of different classes by combining the spectral feature with the spatial feature; 2) the adjustment strategy of each manifold mappings is addressed, which allows not only better visualization of the results, but also the comparisons of mapping results with an appropriate measurement; 3) in order to get useful boundary points used for classification, single threshold and multiple thresholds method are presented to solve small scale and large scale classification problem, respectively. We can easily obtain the expected classification results by adjusting the weights of the two kinds of feature of hyperspectral image. With the LE PD-Flow, variation of pixels on the boundaries for classification can be found, and then hyperspectral data can be labeled with high accuracy. Experimental results show that the proposed method is effective for classification of hyperspectral image.

Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection

This paper presents an image segmentation method named Context-based Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image Segmentation (CHUMSIS), which uses superpixels as the operation units instead of pixels. Based on the Gestalt laws, three rules that realize a new and natural way to manage different kinds of features extracted from SAR images are proposed to represent superpixel context. The rules are prior knowledge from cognitive science and serve as top-down constraints to globally guide the superpixel merging. The features, including brightness, texture, edges, and spatial information, locally describe the superpixels of SAR images and are bottom-up forces. While merging superpixels, a hierarchical unequal merging algorithm is designed, which includes two stages: 1) coarse merging stage and 2) fine merging stage. The merging algorithm unequally allocates computation resources so as to spend less running time in the superpixels without ambiguity and more running time in the superpixels with ambiguity. Experiments on synthetic and real SAR images indicate that this algorithm can make a balance between computation speed and segmentation accuracy. Compared with two state-of-the-art Markov random field models, CHUMSIS can obtain good segmentation results and successfully reduce running time.

A Novel Method for Hyperspectral Image Classification Based on Laplacian Eigenmap Pixels Distribution-Flow

Inspired by a popular deep neural network, i.e., deep belief network (DBN), a novel method for polarimetric synthetic aperture radar (POL-SAR) image classification is proposed in this paper. For the particularity of POL-SAR data, a new type of restricted Boltzmann machine (RBM) is specially defined, which we name the Wishart-Bernoulli RBM (WBRBM), and is used to form a deep network named as Wishart DBN (W-DBN). Numerous unlabeled POL-SAR pixels are made full use of in the modeling of POL-SAR pixels by W-DBN. In addition, the coherency matrix is used directly to represent a POL-SAR pixel without any manual feature extraction, which is simple and time saving. Local spatial information, together with the confusion matrix, is used in this paper to clean the preliminary classification result obtained by the method based on W-DBN. Making full use of the prior knowledge of POL-SAR data and local spatial information, the proposed method overcomes shortcomings of traditional methods, in which they are sensitive to extracted features and slow to execute. The experiments, tested on three POL-SAR data sets, show that the proposed method produces better results and is much faster than traditional methods.