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Third International Symposium on Multispectral image processing and pattern recognition

作者： Liu, Zhili Ma, Jianwen Han, Xiuzhen Zhang, Xiaoye Box 9718 Inst. of Remote Sensing Applications CAS Beijing 100101 China Box 17 Institute of Earth Environment CAS Xi'an 710054 China

China - Japan joint dust project was started in 2001 and carried out a synchronous test to monitor the process of dust storm by using satellite data, to observe dust on sites and to collect total suspend particle (TSP), wind direction, wind intensity by using instruments. Si was tested in laboratory by analysis of collected TSR. LST (Land surface temperature) was retrieved from AVHRR data (96 day/night track imagery data). Resample 1:100,000 scale land use/cover data was used as reference layer during retrieving process. The comparison result of LST and TSP showed that there were good correlations. The result suggested that LST reflected some ground physical feature changes during dust storms and could be used as indicators for predict dust storm.

关键词： remote sensing

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Unsupervised feature extraction and band subset selection techniques based on relative entropy criteria for hyperspectral data analysis

Unsupervised feature extraction and band subset selection te...

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

作者： Arzuaga-Cruz, E Jimenez-Rodriguez, LO Vélez-Reyes, M Univ Puerto Rico ECE Dept Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819449539

Feature extraction, implemented as a linear projection from a higher dimensional space to a lower dimensional subspace, is a very important issue in hyperspectral data analysis. This reduction must be done in a manner that minimizes the redundancy, maintaining the information content. This paper proposes methods for feature extraction and band subset selection based on Relative Entropy Criteria. The main objective of the feature extraction and band selection methods implemented is to reduce the dimensionality of the data maintaining the capability of discriminating objects of interest from the cluttered background. These methods accomplish the described goal by maximizing the difference between the data distribution of the lower dimensional subspace and the standard Gaussian distribution. The difference between the low dimensional space and the Gaussian distribution is measured using relative entropy, also known as information divergence. A Projection Pursuit unsupervised algorithm based on an optimization algorithm of the relative entropy is presented. An unsupervised version for selecting bands in hyperspectral data will be presented as well. The relative entropy criterion will measure the information divergence between the probability density function of the feature subset and the Gaussian probability density function. This augments the separability of the unknown clusters in the lower dimensional space. One advantage of these methods is that there is no use of labeled samples. These methods were tested using simulated data as well as remotely sensed data.

关键词： remote sensing pattern recognition unsupervised classification dimensionality reduction feature extraction band subset selection projection pursuit principal components analysis high dimensional space hyperspectral images

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remote sensing image compression for deep space based on region of interest

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Journal of Harbin Institute of Technology(New Series) 2003年第3期10卷 300-303页

作者：王振华吴伟仁田玉龙田金文柳健 Institute for Pattern Recognition and Artificial Intelligence State Key Lab for Image Processing and Intelligent ControlHuazhong University of Science and Technology Wuhan 430074 China Institute for Pattern Recognition and Artificial Intelligence State Key Lab for Image Processing and Intelligent ControlHuazhong University of Science and Technology Wuhan 430074 China major limitation for deep space communication is the limited bandwidths available. The downlink rate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However the Next Generation Space Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be reduced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image compression techniques. An very low bit rate image compression method based on region of interest(ROI) has been proposed for deep space image. The conventional image compression algorithms which encode the original data without any data analysis can maintain very good details and haven't high compression rate while the modern image compressions with semantic organization can have high compression rate even to be hundred and can't maintain too much details. The algorithms based on region of interest inheriting from the two previews algorithms have good semantic features and high fidelity and is therefore suitable for applications at a low bit rate. The proposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal local quality with bit rate control. The Result shows that our method can maintain more details in ROI than general image compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas

A major limitation for deep space communication is the limited bandwidths available. The downlinkrate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However, the Next GenerationSpace Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be re-duced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image com-pression techniques. An very low bit rate image compression method based on region of interest(ROI) has beenproposed for deep space image. The conventional image compression algorithms which encode the original datawithout any data analysis can maintain very good details and haven' t high compression rate while the modernimage compressions with semantic organization can have high compression rate even to be hundred and can' tmaintain too much details. The algorithms based on region of interest inheriting from the two previews algorithmshave good semantic features and high fidelity, and is therefore suitable for applications at a low bit rate. Theproposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal localquality with bit rate control. The Result shows that our method can maintain more details in ROI than generalimage compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas.

关键词： wavelet compression ROI deep space

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Towards the Bayesian-regularization method for enhanced SAR imaging

Towards the Bayesian-regularization method for enhanced SAR ...

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conference on Algorithms for Synthetic Aperture Radar imagery X

作者： Shkvarko, YV IPN CINVESTAV Guadalajara 45090 Jalisco Mexico

ISBN: (纸本)0819449555

In this paper, we propose to unify the Bayesian estimation strategy with the statistical regularization-based techniques for image reconstruction through developing the fused Bayesian-regularization (FBR) method for the high resolution estimation of the spatial spectrum pattern (SSP) of the wave field scattered from the probing surface. The problem is treated as it is required for enhanced radar imaging of the remotely sensed scenes via processing one sampled realization of the SAR trajectory signal. The derived optimal FBR estimator is a nonlinear solution-dependent (thus referred to as an adaptive) algorithm that also permits a concise robust simplification to the non-adaptive easy-to-implement imaging techniques. The optimal and robustified suboptimal SSP estimation algorithms imply formation of the second order sufficient statistics from the SAR trajectory data signals and their smoothing applying the window operators. The new formalism of such the sufficient statistics and windows explaining their adjustment to the metrics in a solution space, a priori nonparametric model of the desired SSP, its correlation properties and imposed regularization constraints is developed. The advantage in using the proposed method is demonstrated through simulations of enhancing the SAR images using a family of the robustified FBR-based imaging algorithms.

关键词： remote sensing spatial spectrum pattern regularization Bayesian estimation radar imaging

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Adaptive segmentation of remote-sensing images for aerial surveillance

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10th International conference on Computer Analysis of images and patterns, CAIP 2003

作者： Baik, Sung W. Ahn, Sung M. Lee, Jong W. Win, Khin K. Sejong University Seoul143-747 Korea Republic of Kookmin University Seoul136-702 Korea Republic of Yangon University Yangon Myanmar

ISBN: (纸本)3540407308

The paper focuses on the adaptive segmentation of aerial images for aerial surveillance. The adaptive segmentation is achieved by the cooperation of on-line model modification and model based image segmentation through RBF neural network classifier. The on-line model modification allows the RBF classifier to adapt to the changes of geographical features on the aerial images. In addition, the Gabor filtering method for feature extraction is proposed in this experiment to discriminate between geographical features for better image segmentation. © Springer-Verlag Berlin Heidelberg 2003.

关键词： image segmentation

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Two Sequential Stages Classifier for Multispectral Data

Two Sequential Stages Classifier for Multispectral Data

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Refaat M Mohamed Aly A Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

In this paper, we present an approach for the classification of remote sensing multispectral data, which consists of two sequential stages. The first stage exploits the capabilities of the Support Vector Machines (SVM) approach for density estimation and uses it in a Bayes classification setup. In a typical image, the class of a pixel is highly dependent on the classes of its neighbor pixels. The second stage exploits the dependency of the classes. We incorporate this dependency using stochastic modeling of the context as a Markov Random Field (MRF). The MRF is modeled using Besag model and implemented using the Iterative Conditional Modes (ICM) algorithm. Results show that the stochastic modeling approach enhances the results and provides reasonable smoothness in the classified image.

关键词： Support vector machines Estimation Classification algorithms Pixel Density functionional theory Kernel Computer vision

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Contour-based shape representation for image compression and analysis

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11th International conference on Discrete Geometry for Computer imagery

作者： D'Elia, C Scarpa, G Univ Cassino DAEIMI Cassino FR Italy Univ Naples Federico II Dept Elect & Telecommun Engn Naples Italy

ISBN: (纸本)3540204997

With the rapid growth of computing power, many concepts and tools of image analysis are becoming more and more popular in other data processing fields, such as image and video compression. image segmentation, in particular, has a central role in the object-based video coding standard MPEG-4, as well as in various region-based coding schemes used for remote-sensing imagery. A region-based image description, however, is only useful if it has a limited representation cost, which calls for accurate and efficient tools for the description of region boundaries. A very promising approach relies on the extended boundary concept, first discussed in [6] and [7] and later used by Liow [5] to develop a contour tracing algorithm. In this work, we extend Liow's algorithm and introduce the corresponding reconstruction technique needed for coding purposes. In addition, we define an algebraic semi-group structure that allows us to formally prove the algorithm properties, to extend it to other boundary definitions, and to introduce a fast contour tracing algorithm which only requires a raster scan of the image.

关键词： image compression

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Feature Selection of Hyperspectral Data Through Local Correlation and SFFS for Crop Classification

Feature Selection of Hyperspectral Data Through Local Correl...

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2003 IEEE IGARSS: Learning From Earth's Shapes and Colours

作者： Gomez-Chova, L. Calpe, J. Camps-Valls, G. Martín, J.D. Soria, E. Vila, J. Alonso-Chorda, L. Moreno, J. University of Valencia Valencia Spain

In this paper, we propose a procedure to reduce dimensionality of hyperspectral data while preserving relevant information for posterior crop cover classification. One of the main problems with hyperspectral image processing is the huge amount of data involved. In addition, pattern recognition methods are sensitive to problems associated to high dimensionality feature spaces (referred to as Hughes phenomenon or curse of dimensionality). We propose a dimensionality reduction strategy that eliminates redundant information by means of local correlation criterion between contiguous spectral bands;and a subsequent selection of the most discriminative features based on a Sequential Float Feature Selection algorithm. This method is tested with a crop cover recognition application of six hyperspectral images from the same area acquired with the 128-bands HyMap spectrometer during the DAISEX99 campaign. In the experiments, we analyze the dependence on the dimension and employed metrics. The results obtained using Gaussian Maximum Likelihood improve the classification accuracy and confirm the validity of the proposed approach. Finally, we analyze the selected bands of the input space in order to gain knowledge on the problem and to give a physical interpretation of the results.

关键词： remote sensing

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conference on Computer Vision and pattern recognition (CVPR)

作者： Y. Keller A. Averbuch Department of Computer Science Technion-Israel Institute of Technology Haifa Israel Department of Computer Science School of Mathematical Sciences Tel-Aviv University Tel-Aviv Israel

This paper presents an implicit similarity-based approach to registration of significantly dissimilar images, acquired by sensors at different modalities. The proposed algorithm introduces a robust matching criterion by aligning the locations of gradient maxima. The alignment is formulated as a parametric variational optimization problem, which is solved iteratively by considering the intensities of a single image. The location of the maxima of the second image's gradient are used as initialization. We are able to robustly estimate affine and projective global motions using 'coarse to fine' processing, even when the images are characterized by complex space varying intensity transformations. Finally, we present the registration of real images, which were taken by multi-sensor and multi-modality using affine and projective motion models.

关键词： image registration image sensors Robustness Magnetic resonance imaging Gradient methods Computer science Sensor phenomena and characterization remote sensing Biomedical imaging Computed tomography

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Structure-context based fuzzy neural network approach for automatic target detection

Structure-context based fuzzy neural network approach for au...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Qu Jishuang Wang Chao Wang Zhengzhi Inst. of Mechatronics Engineering and Automation National University of Defense Technology Changsha Hunan China Key Lab of Remote Sensing Information Institute of Remote Sensing Applications Chinese Academy and Sciences Beijing China

ISBN: (纸本)0780379292

This paper proposes a structure-context based fuzzy neural network (SCBFNN) approach for automatic target detection. Fuzzy neural network methods not only possess advantages as adaptivity, parallelism, robustness, ruggedness, optimality, but integrate advantages as depicting and solving system uncertainty by fuzzy set theory, accordingly, they are powerful tools for image processing and pattern recognition. Use fuzziness measures as objective function of neural network can depict uncertainty of pixel's category validly so as to optimize image classification by minimizing the objective function. Put information constraint of structure context on neurons' weighting processing can reduce loss of image information, especially, the rich information comprised by target edges, by which target's attributes such as profile and shape can be retained validly, and the false detection rate can also be improved prominently. Experiments on remote sensed images of target are executed to validate SCBFNN approach, and the results exhibit that SCBFNN possesses good ability to automatic target detection, simultaneously, possesses valid abilities to eliminating uncertainty and retaining target shape compared with conventional neural network methods. At last, a brief conclusion is given.

关键词： Fuzzy neural networks Object detection Neural networks image edge detection Shape Robustness Fuzzy set theory image processing pattern recognition Pixel

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