The direct evidence of Radar Target recognition is the backscatter energy and its distribution of objects, which is concluded by imaging process of Synthetic Aperture Radar (SAR) in the two-dimension image domain. So ...
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In recent years, observation of a wide variety in the Earth's surface can be done by improvement of remotesensing technology. The purpose of the paper is to recognize a drift ice as thick ice, thin ice, and sea u...
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In recent years, observation of a wide variety in the Earth's surface can be done by improvement of remotesensing technology. The purpose of the paper is to recognize a drift ice as thick ice, thin ice, and sea using synthetic aperture radar (SAR) images. The recognition of the drift ice is achieved by using neural networks (NN). The neural network applies two methods, a BP trained neural network and a self-organizing map. Training data are image features extracted from SAR images. There are three methods for extracting the features: Fourier transform, high-order autocorrelation function (HACF), and image features based on a run length method. We carry out a comparative experiment, and demonstrate their effectiveness by means of computer simulation.
Addresses issues related to classification of images in complex spaces. The image is represented in terms of phase and amplitude components. The classifier optimizes functions of joint real and imaginary conditional p...
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Addresses issues related to classification of images in complex spaces. The image is represented in terms of phase and amplitude components. The classifier optimizes functions of joint real and imaginary conditional probability density functions. A bound on the total probability of errors in terms of Rayleigh quotient is derived and compared to the cases where a non-complex amplitude-only signal is used. Examples of application of the proposed approach on polarimetric radar imagery indicate several orders of magnitude improvement in performance.
Artificial neural networks (ANN) constitute a powerful class of nonlinear function approximate for model-free estimation. ANN has been widely used in patternrecognition, prediction and classification. In the artifici...
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ISBN:
(纸本)1889335185
Artificial neural networks (ANN) constitute a powerful class of nonlinear function approximate for model-free estimation. ANN has been widely used in patternrecognition, prediction and classification. In the artificial neural network approach, we compare radial basis function neural networks (RBFNN) and wavelet neural networks for multispectral image classification. The aim of this study is to examine the effectiveness of the neural network model for multispectral image classification. Radial basis function neural network is used for its advantages of rapid training, generality and simplicity over feedforward backpropagation neural network. The k-means clustering is used to choose the initial radial basis centers and widths for the RBFNN. The wavelet is a localized function that is capable of detecting some features in signals. A wavelet basis function is assigned for each neuron and each synaptic weight is determined by learning. An attempt is also made to study the performance of the RBFNN with the centers and widths chosen using the classical k-means clustering.
With the development of remotesensing technique, onboard data compression has become an urgent need and a lot of study has been directed toward the development of efficient techniques. In this paper, the construction...
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With the development of remotesensing technique, onboard data compression has become an urgent need and a lot of study has been directed toward the development of efficient techniques. In this paper, the construction approach of integer Haar is discussed briefly, and a simple image compression scheme based on integer Haar Wavelet Transform and block DPCM is proposed. The scheme can be easily designed for data processing in real-time system of remotesensing with parallel algorithm. Simulation experimental results demonstrate that the proposed approach is a efficient image compression method.
The direct evidence of radar target recognition is the backscatter energy and its distribution of objects, which is concluded by the imaging process of synthetic aperture radar (SAR) in a two-dimension image domain. T...
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ISBN:
(纸本)076951695X
The direct evidence of radar target recognition is the backscatter energy and its distribution of objects, which is concluded by the imaging process of synthetic aperture radar (SAR) in a two-dimension image domain. Thus, the issues about automatic target recognition of SAR are often a "post-process" following SAR imaging. A method of target detection in the state of non-imaging based on analyzing the features of all kinds of targets on SAR echo data is presented in this paper, where the targets are defected in range-Doppler domain (RDD). Based on the analysis of target features in RDD, an algorithm of target detection in a RDD image is developed. The experimental results indicate the validity of the algorithm for special kinds of target detection. The algorithm is simple, able to recurrent, easy for real-time processing and hardware implementation. It can also be applied to getting target alarm with real-time SAR data for the pre-selected targets and do the imaging process selectively at the same time.
This paper presents a metric based on information theory principles that compares 3D object models to images. The metric is based on the formulation of the mutual information between the model and the images. The tech...
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This paper presents a metric based on information theory principles that compares 3D object models to images. The metric is based on the formulation of the mutual information between the model and the images. The technique does not require a priori information about the surface properties of the object and is robust with respect to variations of illumination. As a result the method is quite general and may be used in a wide variety of applications. Experiments are presented that demonstrate the approach by evaluating reconstructed 3D building models in aerial images.
In this paper, a new segmentation technique for multivalued images is elaborated. The technique accesses multiscale edge information of a multivalued image by a concept, called multiscale fundamental form. At differen...
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ISBN:
(纸本)076951695X
In this paper, a new segmentation technique for multivalued images is elaborated. The technique accesses multiscale edge information of a multivalued image by a concept, called multiscale fundamental form. At different scales, an edge map of the multivalued images is obtained, at which a watershed-based algorithm is applied. The multiscale behavior of the obtained watershed regions is employed to conduct a region merging procedure. In order to remove noise or local texture, prior to segmentation an anisotropic diffusion filter is applied, also making use of the multiscale fundamental forms. In this way, the entire procedure is applied using multivalued processing.
The following topics are dealt with: power system modelling, planning and operation; power electronics and machines; electromagnetics and optics; antenna theory, design and applications; remotesensing; SAR ; acoustic...
The following topics are dealt with: power system modelling, planning and operation; power electronics and machines; electromagnetics and optics; antenna theory, design and applications; remotesensing; SAR ; acoustic ranging; microelectronics and VLSI; nanotechnology and micromachining; instrumentation and sensors; circuits and systems; robotics and mechatronics; reliability engineering; computers and digital hardware; real-time systems; software and information technology; computational intelligence; neural networks; genetic algorithms and fuzzy logic; patternrecognition; imageprocessing; video processing; signal processing and filter design; biomedical engineering; health-care systems; communications systems; computer networks; wireless networks; telecommunication traffic analysis; QoS; industrial applications.
Summary form only given. Signal processing has now had many benefits to our daily life though we may not be aware of. For examples: microprocessors (a kind of signal processor) are used to control gas consumption to m...
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Summary form only given. Signal processing has now had many benefits to our daily life though we may not be aware of. For examples: microprocessors (a kind of signal processor) are used to control gas consumption to make our cars more fuel efficient and signal processing is used to predict the major weather changes. Signal processing has played important roles in our environment including land water and air, through monitoring, control and prediction functions. In this presentation, the recent progress of signal processing, including hardware, software and algorithms, the neural networks, and the roles of remotesensing, will be reviewed first. This is followed by presenting several examples of using signal processing to preserve our environment. Some notable examples are: monitoring of landmines, pollutants in waterways, the salinity in the coastal zones, and the changes in environment; control of oil spills, flood mapping and control; inspection of damages of forest fires; prediction of major storms; etc. It is noted that signal processing is closely linked to patternrecognition in many of these examples. Signal processing in conjunction with patternrecognition will play an increasingly important role in all of these activities that aim to preserve our precious environmental resources.
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