The alignment of images acquired using the synthetic aperture radar (SAR) is a central task in image processing for remote sensing. Owing to speckle noise and the particular features of SAR imaging, stable feature detection and accurate matching remain challenging tasks. We propose a registration method for SAR images. In feature detection, we develop a hybrid feature detection method to identify structural and textural features. A stable convex corner point feature is detected using stable convex polygons obtained by a proposed method for local stable extremal region extraction and convex polygon fitting. The stable local extremum points in each convex polygon are detected by an improved scale invariant feature transform method. In feature matching, a multifeature constraint matching method is designed for accurate matching. Coarse matching is implemented using the constraints on the region and its shape as well as the network. The use of stable local extremum points with spatial constraints can eliminate mismatches and yield a fine match. The results of experiments verified the effectiveness and accuracy of the proposed method.
Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.
Proc. SPIE. 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis
KEYWORDS: Signal to noise ratio, Principal component analysis, Image compression, Chemical species, Denoising, Interference (communication), Image quality, Associative arrays, Image denoising, Signal analyzers
This article addresses the image denoising problem in the situations of strong noise. The method we propose is intended to preserve faint signal details under these difficult circumstances. The new method we introduce, called principal basis analysis, is based on a novel criterion: the reproducibility which is an intrinsic characteristic of the geometric regularity in natural images. We show how to measure reproducibility. Then we present the principal basis analysis method, which chooses, in sparse representation of the signal, the components optimizing the reproducibility degree to build a so-called principal basis. With this principal basis, we show that a noise-free reconstruction may be obtained. As illustrations, we apply the principal signal basis to image denoising for natural images with details in low signal-to-noise ratio, showing performance better than some reference methods.
This paper presents a target detection method in synthetic aperture radar (SAR) images with radiometric multiresolution analysis (RMA). The idea is that target saliency can be efficiently computed by comparing the statistics of targets and those of the local background around them. In order to compute reliable statistics of targets, which usually involve a small number of pixels, RMA is adopted. The RMA preprocessing method performs well in stabilizing the statistical characteristics of SAR images. It can effectively restrain the speckle noise while keep the statistical characteristics of the original image. Based on the computed target saliency, adaptive decision thresholds are got by using the constant false alarm rate (CFAR) target detection framework. Our experiments on real SAR images show that the proposed method can achieve better performance compared with the traditional cell average-constant false alarm rate (CA-CFAR) method.
This paper aims at the extraction of roads and road network from high-resolution dual-polarization synthetic aperture
radar data over urban areas. According to the different features and applications for road network, the errors will be
brought in the detection algorithm if it was not selected correctly. We proposed a modified extraction method making full
use of available information to reduce such errors. In particular, we want to show how to implement road extraction
algorithms based on the D-S evidence theory to establish the frame of discernment. The responses of two line detectors
at the local analysis process were combined, which was done at the feature level by balancing the weight of two
propositions constructed by responses of two line detectors. Then the road network optimization is accomplished using a
Markov random field model of road, where both some contextual knowledge and global constraints were taken into
account. The experimental results indicate that the proposed method is promising for main roads detection of urban
Particle filter has attracted much attention due to its robust tracking performance in clutter. However, a price to pay for its robustness is the computational cost. Meanwhile there is no exact mechanism for choosing or updating scale in its framework for accurate tracking. In this paper we propose a threshold and scale based particle filter (TSPF). It employs a threshold to discard the bad particles and keep the good ones. In this case, the efficiency of particles is improved and the number of required particles is greatly reduced. It also adapts Robert T. Collins's theory of selecting kernel scale for mean shift blob tracking to particle filter. Experiments show TSPF works well, both spatially and in scale.
This paper investigates turbo equalization (TE) with linear minimum mean squared error (LMMSE) equalizer, which has much lower complexity but much worse performance than TE with maximum a posteriori (MAP) equalizer because of inefficient information processing of LMMSE equalizer. Motivated by forward and backward recursions of MAP algorithm, three methods are presented to improve turbo equalization with LMMSE equalizer (TE-LMMSE). In the simulation, they all obtain more than 2.5-dB performance gain over TE-LMMSE with no or some complexity price.
A wideband high frequency (HF) channel simulator is implemented in a software simulation of a direct-sequence spread-spectrum (DSSS) HF communication system. A RAKE receiver is used to mitigate the effects of multipath delay. The validity of the HF channel simulator has been demonstrated by the analysis of in-out signals of the channel simulator. It shows that the simulator accurately models a HF channel. New performance results of the system with convolutional codes and bit interleaving are advanced to demonstrate the potential of the system with proposed scheme.
In this paper, an approach of edge detection based-on multifractal is proposed. We apply the 2D wavelet transform modulus maxima (WTMM) method to characterize pointwise Holder regularity and the multifractal spectrum, so edge information can be extracted directly from them. Experiment results demonstrate that multifractal based edge detection has strong flexibility and good detection effect.
Propose one simple and efficient multi-level thresholding method. Basic dynamic is used to assess the reliability of thresholds. All possible thresholds are detected and sorted by assessment value calculated in water flooding process. Basing on the sorted threshold sequence, when level number changes, thresholds need not be recalculated, and multiple results can be got efficiently. Experimental results are satisfactory.
In this paper, we present an unsupervised texture segmentation algorithm for Synthetic aperture radar (SAR) images based on a multiscale modeling over images in wavelet pyramidal structure. An image consisting of different textures can be considered as a realization of a collection of two interacting random process-the hidden region label process and the observation process. A novel Gaussian Markov random field (GMRF) model is proposed to describe the fill-in of regions at each scale and a multi-level logistic (MLL) MRF model with particular cliques is used to characterize the intrascale and interscale context dependencies. According to sequential maximum a posterior (SMAP) estimate, expectation-maximization (EM) algorithm is adopted to estimate the parameters of GMRF and to label each pixel iteratively from coarse to fine level. The proposed segmentation approach is applied to synthetic image and SAR image and the result shows its performance.
Discrete-Amplitude Multiresolution Analysis (DAM) is a new kind of multiresolution analysis that uses signal quantization resolution as its scale. In this paper, a new efficient image coding method that utilizes the DAM's 2 bits recomposing theory of arbitrary continuous signal is realized. The problem of how to choose the coding direction, which is important for the 2D image data while not exists in the 1D data processing, is emphasized. The problems appeared with the application of DAM on image data are also analyzed. The experimental results proved that DAM image coding is a simple, easy-approaching and efficient coding method and it should have more potential application in image processing.