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Fractal analysis of remotely sensed images: A review of methods and applications

INTERNATIONAL JOURNAL OF remote sensing 2006年第22期27卷 4963-4990页

作者： Sun, W. Xu, G. Gong, P. Liang, S. Grand Valley State Univ Dept Geog & Planning Allendale MI 49401 USA Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Univ Maryland Dept Geog College Pk MD 20742 USA

Mandelbrot's fractal geometry has sparked considerable interest in the remote sensing community since the publication of his highly influential book in 1977. Fractal models have been used in several image processing and pattern recognition applications such as texture analysis and classification. Applications of fractal geometry in remote sensing rely heavily on estimation of the fractal dimension. The fractal dimension (D) is a central construct developed in fractal geometry to describe the geometric complexity of natural phenomena as well as other complex forms. This paper provides a survey of several commonly used methods for estimating the fractal dimension and their applications to remote sensing problems. Methodological issues related to the use of these methods are summarized. Results from empirical studies applying fractal techniques are collected and discussed. Factors affecting the estimation of fractal dimension are outlined. Important issues for future research are also identified and discussed.

关键词： remote sensing

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Commodity cluster and hardware-based massively parallel implementations of hyperspectral imaging algorithms

Commodity cluster and hardware-based massively parallel impl...

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conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery XII

作者： Plaza, Antonio Chang, Chein-, I Plaza, Javier Valencia, David Univ Extremadura Comp Sci Dept Avda Univ S-N E-10071 Caceres Spain Univ Maryland Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)0819462896

The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several applications exist, however, where having the desired information calculated quickly enough for practical use is highly desirable. High computing performance of algorithm analysis is particularly important in homeland defense and security applications, in which swift decisions often involve detection of (sub-pixel) military targets (including hostile weaponry, camouflage, concealment, and decoys) or chemical/biological agents. In order to speed-up computational performance of hyperspectral imaging algorithms, this paper develops several fast parallel data processing techniques. Techniques include four classes of algorithms: (1) unsupervised classification, (2) spectral unmixing, and (3) automatic target recognition, and (4) onboard data compression. A massively parallel Beowulf cluster (Thunderhead) at NASA's Goddard Space Flight Center in Maryland is used to measure parallel performance of the proposed algorithms. In order to explore the viability of developing onboard, real-time hyperspectral data compression algorithms, a Xilinx Virtex-II field programmable gate array (FPGA) is also used in experiments. Our quantitative and comparative assessment of parallel techniques and strategies may help image analysts in selection of parallel hyperspectral algorithms for specific applications.

关键词： hyperspectral imaging parallel computing commodity clusters field programmable gate arrays

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A Higher-Order Active Contour Model for Tree Detection

A Higher-Order Active Contour Model for Tree Detection

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International conference on pattern recognition

作者： P. Horvath I. Jermyn J. Zerubia Z. Kato Ariana joint research group CNRS/INRIA/UNSA INRIA Sophia-Antipolis France Institute of Informatics University of Szeged Szeged Hungary

We present a model of a `gas of circles', the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short range repulsive interactions, and apply it to the extraction of tree crowns from aerial images. The method uses the recently introduced `higher order active contours' (HOACs), which incorporate long-range interactions between contour points, and thereby include prior geometric information without using a template shape. This makes them ideal when looking for multiple instances of an entity in an image. We study an existing HOAC model for networks, and show via a stability calculation that circles stable to perturbations are possible for constrained parameter sets. Combining this prior energy with a data term, we show results on aerial imagery that demonstrate the effectiveness of the method and the need for prior geometric knowledge. The model has many other potential applications

关键词： Active contours Data mining Geometry Forestry Informatics Shape Stability remote sensing image processing Muscles

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Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model

Wavelet denoising of multicomponent images, using a Gaussian...

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International conference on pattern recognition

作者： P. Scheunders S. De Backer Vision Laboratory Department of Physics University of Antwerp Belgium

In this paper, denoising on multicomponent images is performed. The presented procedure is a spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using a prior model for the wavelet coefficients that account for the inter-correlations between the multicomponent bands. The applied prior model for the multicomponent signal is a Gaussian scale mixture (GSM) model. The method is compared to single-band wavelet denoising and to multiband denoising using a Gaussian prior. Experiments on a Land-sat multispectral remote sensing image are conducted

关键词： Noise reduction GSM Filtering Low pass filters remote sensing Hyperspectral imaging Filter bank Bayesian methods Principal component analysis Discrete wavelet transforms

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Radar Signal Classification Using Pca-Based Features

Radar Signal Classification Using Pca-Based Features

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： A.K. Mishra B. Mulgrew IDCOM University of Edinburgh Edinburgh UK

Principal component analysis (PCA) has been used in many applications ranging from social science to space science, for the purpose of data compression and feature extraction. Usage of PCA for synthetic aperture radar (SAR) image classification, though widely reported by remote-sensing researchers, has not been exploited much by automatic target recognition (ATR) community. In the present paper, PCA has been used in SAR-ATR using the MSTAR data base, and comparison has been made with the conventional conditional Gaussian model based Bayesian classifier (M.D. DeVore and J.A. O'Sullivan, 2002). The results have been compared based on percentage of correct classification, receiver operating characteristics (ROC), and performance with limited amount of training data. By all standards of comparison, the PCA based classifier was observed to outperform the conditional Gaussian model based Bayesian classifier (CGBC) or at the worst it performs at par. And given the computational and algorithmic simplicity of PCA based classifier, the new algorithm was concluded to be a highly prospective candidate for real time ATR systems

关键词： pattern classification Principal component analysis Bayesian methods Radar imaging Radar remote sensing Data compression Feature extraction Synthetic aperture radar image classification remote sensing

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Unifying the Experiment Design and Constrained Regularization Paradigms for Reconstructive Imaging with remote sensing Data

Unifying the Experiment Design and Constrained Regularizatio...

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IEEE International conference on image processing

作者： Yuriy V. Shkvarko Jose L. Leyva-Montiel Ivan E. Villalon-Turrubiates CINVESTAV Zapopan Jalisco Mexico

In this paper, the problem of estimating from a finite set of measurements of the radar remotely sensed complex data signals, the power spatial spectrum pattern (SSP) of the wavefield sources distributed in the environment is cast in the framework of Bayesian minimum risk (MR) paradigm unified with the experiment design (ED) regularization technique. The fused MR-ED regularization of the ill-posed nonlinear inverse problem of the SSP reconstruction is performed via incorporating into the MR estimation strategy the projection-regularization ED constraints. The simulation examples are incorporated to illustrate the efficiency of the proposed unified MR-ED technique.

关键词： image reconstruction remote sensing Space power stations Power measurement Radar imaging Radar measurements Radar remote sensing Bayesian methods Signal design Inverse problems

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2006 IEEE International conference on image processing

2006 IEEE International Conference on Image Processing

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IEEE International conference on image processing

The following topics are dealt with: source/channel coding; distributed image and video coding; biomedical image segmentation; steganography and steganalysis; content summarization and clustering; fingerprint and iris analysis; image registration/alignment and mosaicking; stereoscopic and 3-D coding; visual tacking; deblurring and image restoration; face/facial expression detection and recognition; interpolation and inpainting; network-aware multimedia processing and communications; edge detection; transcoding; machine learning; image fusion; video networking and communications; watermarking; low-level indexing and retrieval of images; wavelets and filter banks; video streaming; video surveillance; soft computing in image processing; authentication and cryptography; forensics; radar imaging; block matching-based motion estimation; knowledge-based image processing for classification and recognition; biometrics; magnetic resonance imaging; image enhancement; image quality assessment; 3DTV: extraction, representation, compression and transmission; remote sensing.

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A Key Frame Selection-Based Facial Expression recognition System

A Key Frame Selection-Based Facial Expression Recognition Sy...

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International conference on Innovative Computing, Information and Control (ICICIC)

作者： S.M. Guo Y.A. Pan Y.C. Liao C.Y. Hsu J.S.H. Tsai C.I. Chang Department of Computer Science and Information Engineering National Engineering Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan Republic of China Department of Computer Science and Electrical Engineering University of Maryland Baltimore County (UMBC) Baltimore MD U.S.A. National Cheng Kung University Tainan TW Department of Electrical Engineering National Engineering Kung University Tainan Taiwan Department of Electrical Engineering National Cheng-Kung University Tainan Taiwan Republic of China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore Baltimore MD USA

Automatic recognition of facial expressions has received considerable interest in the computer vision. This paper presents a key frame selection-based system for recognition of four basic facial expressions: happiness, anger, sadness and surprise, in image sequences. The proposed new approach is designed for recognition of facial expressions via a key frame selection of the facial motion from an image sequence where an optical flow is used to determine changes in a facial expression, experimental results demonstrate that the proposed automatic recognition process significantly improves the computational complexity encountered in facial expression recognition while enhancing the ability of recognition

关键词： Face recognition Face detection image recognition image sequences Feature extraction Facial features Computer science remote sensing Signal processing image processing

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Adaptative Markov Random Fields for Omnidirectional Vision

Adaptative Markov Random Fields for Omnidirectional Vision

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International conference on pattern recognition

作者： C. Demonceaux P. Vasseur C.R.E.A. (Centre de Robotique d'Electrotechnique et d'Automatique)-EA 3299 Université de Picardie Jules-Verne Amiens France

images obtained with catadioptric sensors contain significant deformations which prevent the direct use of classical image treatments. Thus, Markov random fields (MRF) whose usefulness is now obvious for projective image processing, cannot be used directly on catadioptric images because of the inadequacy of the neighborhood. In this paper, we propose to define a new neighborhood for MRF by using the equivalence theorem developed for central catadioptric sensors. We show the importance of this adaptation for a motion detection application

关键词： Markov random fields image processing Cameras image sensors Mirrors Motion detection image segmentation Robot sensing systems Sensor systems Optical network units

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Bijective image Registration using Thin-Plate Splines.

Bijective Image Registration using Thin-Plate Splines.

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International conference on pattern recognition

作者： A.P. Eriksson K. Astrom Centre for Mathematical Sciences Lund University Lund Sweden

image registration is the process of geometrically aligning two or more images. In this paper we describe a method for registering pairs of images based on thin-plate spline mappings. The proposed algorithm minimizes the difference in gray-level intensity over bijective deformations. By using quadratic sufficient constraints for bijectivity and a least squares formulation this optimization problem can be addressed using quadratic programming and a modified Gauss-Newton method. This approach also results in a very computationally efficient algorithm. Example results from the algorithm on three different types of images are also presented

关键词： image registration Computer vision Least squares methods Constraint optimization Quadratic programming Newton method Recursive estimation remote sensing Biomedical imaging Deformable models

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