Classification of targets by micro-Doppler signatures has attracted a growing interest in recent years. The main bulk translation of a target and additional target motions, such as vibrations and rotations, generate D...
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ISBN:
(纸本)9781424489022
Classification of targets by micro-Doppler signatures has attracted a growing interest in recent years. The main bulk translation of a target and additional target motions, such as vibrations and rotations, generate Doppler modulations in the echo that contain unique target features and thus can be used to perform target recognition. Although, target classification by micro-Doppler signatures has been exploited in the RF regime for radar systems, the frequency spectrum is becoming increasingly congested and expensive to use, so that it is desirable to identify and exploit other types which have similar capabilities. In this paper a frequency-agile non-coherent ultrasound radar developed to gather micro-Doppler signatures is presented. This was used in an experimental trial to gather micro-Doppler signatures of personnel targets whilst undertaking various types of motion. Classification performance by these same micro-Doppler signatures is then assessed and results discussed.
In this paper, a technique is presented for the fusion of Panchromatic (PAN) and low spatial resolution multispectral (MS) images to get high spatial resolution of the latter. In this technique, we apply PCA transform...
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In this paper, a technique is presented for the fusion of Panchromatic (PAN) and low spatial resolution multispectral (MS) images to get high spatial resolution of the latter. In this technique, we apply PCA transformation to the MS image to obtain the principal component (PC) images. A NSCT transformation to PAN and each PC images for N level of decomposition. We use FOCC as criterion to select PC. And then, we use the relative entropy as criterion to reconstruct high-frequency detailed images. Finally, we apply inverse NSCT to selected PC's low-frequency approximate image and reconstructed high- frequency detailed images to obtain high spatial resolution MS image. The experimental results obtained by applying the proposed image fusion method indicate some improvements in the fusion performance.
We present a system to automatically estimate the body pose of a reclined patient, based on measurement data from a pressure sensing mattress. It can be used to replace or reduce manual input in clinical imaging proce...
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Data acquisition and storage is an important step in the process in building GIS system. In the process of building a database, using the method of intelligent automatic vectorization is of great significance for redu...
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Data acquisition and storage is an important step in the process in building GIS system. In the process of building a database, using the method of intelligent automatic vectorization is of great significance for reducing the workload of building a database and improving working efficiency. This paper presents a method of point patternrecognition based on interactive approach, that is to say, the way of creating punctuate ground feature on the grid thematic map and using the model to identify the same other model of punctuate background feature on the thematic map. Practice has proved that the algorithm in this paper can help the recognition rate reach 95% for the stable model thematic mapsand then improve the data collecting efficiency of pinctuate ground feature.
Single image haze removal (or de-haze) using dark channel prior model is effective when there exists a dark channel within the image. So for the images which do not meet the dark channel prior, the haze removal result...
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Single image haze removal (or de-haze) using dark channel prior model is effective when there exists a dark channel within the image. So for the images which do not meet the dark channel prior, the haze removal result may appear light pollution, cross-color. In this paper, we propose an algorithm to judge whether a single image meets the dark channel prior, and for the image that fails to meet the prior, it eliminates the cross-color influence in the image after de-hazing. Experimental results show that ours can determine whether the image meets dark channel prior, and has the better de-haze effect.
Standard backprojection technique is used in typical through-wall synthetic aperture radar (TWSAR) image, it implicitly assumes infinite beamwidth, whereby the entire target area is illuminated and returns are collect...
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Standard backprojection technique is used in typical through-wall synthetic aperture radar (TWSAR) image, it implicitly assumes infinite beamwidth, whereby the entire target area is illuminated and returns are collected from all points in the imaging grid, and this decreases the efficiency of the algorithm by summing over unnecessary grid points. In order to eliminate the disadvantage, the time-domain backprojection method based on finite beamwidth processing is proposed in the paper. The processing method is designed to be more physically realistic by accounting for the actual antenna pattern. Simulation results show that depending on the width of the effective beam, the number of processing loops can be reduced by up to 50%, while still maintaining good image quality in terms of the reconstructed target response.
With the rapid development of remotesensing technology, the information contained in an image becomes more and more comprehensive. Therefore, how to automatically extract information of interest from image data has b...
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With the rapid development of remotesensing technology, the information contained in an image becomes more and more comprehensive. Therefore, how to automatically extract information of interest from image data has become a hot topic. In this paper, the automatic airborne remotesensingimage classification algorithm is studied by the use of image pyramid. First, the airborne remotesensingimage is divided into several tiles to reduce the amount of data processed at the same time; second, Gaussian smoothing is carried in the studied area in order to weaken the influence of noise; third, image pyramid is established according to the different image scale of the target area; fourth, Canny boundary recognitionprocessing is done in the original image; in the end, a package of stable boundary information is obtained by analyzing the objects' features in different image scale based on the theory "different objects have different performance under different resolutions". Aerial photographs are taken for experiment, the results prove that the algorithm can classify the boundary information effectively. The final classification results can be applied to a series of operations after lined edge procession.
In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last y...
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In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, there is a need for faster and more precise patternrecognition algorithms in or- der to perform online and offline training and classification procedures. We deal here with the problem of moist area classification in radar image in a fast manner. Experimental results using Optimum-Path Forest and its training set pruning algorithm also provided and discussed.
Many studies [1]-[2] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem. Moreover, ke...
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Many studies [1]-[2] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem. Moreover, kernel-based methods have attracted much attention in the area of patternrecognition and machine learning, many researches [3]-[5] show that kernel method is computationally efficient, robust, and stable for pattern analysis. In this study, a novel method which automatically determines the coefficient of the composite kernel [5] that was proposed to join both spectral and spatial information for hyperspectral image classification via an optimail method for selecting an proper kernel function is proposed. The experimental results display the better performance of classification via the composite kernel with this novel method to determine the coefficient than using the RBF kernel function with 5-fold cross-validation method and optimal method to select proper parameter on the famous hyperspectral images, Washington DC Mall.
We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surf...
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We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use task-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminant Analysis produces a linear transform that optimally separates a labeled set of training classes. This defines a distance metric that generalizes to new scenes, enabling graph-based segmentations that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.
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