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4th International Workshop on pattern recognition in remote sensing

作者： Zhou, Jun Cheng, Li Bischof, Walter F. Natl ICT Australia Canberra Lab Canberra ACT 2612 Australia Australian Natl Univ Res Sch Informat Sci & Engn Canberra ACT 0200 Australia Univ Alberta Dept Computing Sci Edmonton AB T6G 2E8 Canada

Current image processing and pattern recognition algorithms are not robust enough to make automated remote sensing image interpretation feasible. For this reason, we need to develop image interpretation systems that rely on human guidance. In this paper, we tackle the problem of semiautomatic road tracking in aerial photos. We propose an online learning approach that naturally integrates inputs from human experts with computational algorithms to learn road tracking. Human inputs provide the online learner with training examples to generate road predictors. An ensemble of road predictors is learned incrementally and used to automatically track roads. When novel situations are encountered, control is returned back to the human expert to initialize a new training and tracking iteration. Our approach is computationally efficient, and it can rapidly adapt to dynamic situations where the image feature distributions change. Experimental results confirm that our approach is effective and superior to existing methods.

关键词： aerial images human-computer interaction (HCI) image interpretation novelty detection online learning road tracking

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remote sensing and GIS application for earth observation on the base of the neural networks in aerospace image classification

Remote sensing and GIS application for earth observation on ...

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3rd International conference on Recent Advances in Space Technologies

作者： Salahova, Saida Azerbaijan Natl Aerosp Agcy Baku Azerbaijan

ISBN: (纸本)9781424410569

Up-to-date curetted task of the present geoinformation system is the processing of the remote sensing data. image analyses in point of mathematical view bases on the theory of the image recognition where it is necessary identification of the input data to the appropriate classis of the objects. Beside with methods of fuzzy logic one of the advance methods of solution the foregoing problem is wide application of Neural Networks in the area. These methods have been taken an adequate instrument which can be described regulation of classification without application of the high accuracy mathematical value as it accepted term for understanding like "small", "significant" etc. There are two approaches of image recognition based on the spectral and spatial characteristics of the investigated class of objects. It is expedient the combination of the both approaches in order to increase an accuracy of recognition and classification of scanned area with complex relief (highlands, settlements, mixed forest, etc.). Each selected individual elements of landscape shall be fount out based on the image fragment analyses, their forms, colors, correlation, and heterogeneity. Neural Network allows assessing interrelation of each selected individual elements of landscape. One of the advantages of the Neural Network is that all elements have an ability to operate in parallel that is essentially increasing the efficiency of problem solution, especially in the area of the image processing. Within the framework of this studies have been offered the approach of development of the Artificial Neural Networks with Back-propagation Error where there is a solution of the problem of creation decoding indicators based on superposition of spectral and spatial characteristics of image of Landsat TM satellite. During the study of classification of the object elements considered learning access is known as the indicator of identification of relation of the classes. It is required development of the Neu

关键词： artificial neural networks data processing image classification remote sensing and GIS satellite image spectral and spatial characteristics

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Material classification using passive polarimetric imagery

Material classification using passive polarimetric imagery

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IEEE International conference on image processing (ICIP 2007)

作者： Thilak, Vimal Creusere, Charles D. Voelz, David G. New Mexico State Univ Klipsch Sch Elect & Comp Engn Las Cruces NM 88003 USA

ISBN: (纸本)9781424414369

Passive imaging polarimetry has emerged as an useful tool in many remote sensing applications including material classification, target detection and shape extraction. In this paper we present a method to classify specular objects based on their material composition from passive polarimetric imagery. The proposed algorithm is built on an iterative model-based method to recover the complex index of refraction of a specular target from multiple polarization measurements. The recovered parameters are then used to discriminate between objects by employing the nearest neighbor rule. The effectiveness of the proposed method is validated with data collected in laboratory conditions. Experimental results indicate that the classification approach is highly effective for distinguishing between various targets of interest. Most significantly, the proposed classification method is robust to a wide range of observational geometry.

关键词： remote sensing illumination invariant object recognition passive polarimetry material classification Stokes vector

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Domain-partitioning RankBoost for face recognition

Domain-partitioning RankBoost for face recognition

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IEEE International conference on image processing (ICIP 2007)

作者： Yao, Bangpeng Ai, Haizhou Ijiri, Yoshihisa Lao, Shihong Tsinghua Univ Comp Sci & Technol Dept Beijing 100084 Peoples R China Omron Corp Sensing & Control Technol Kyoto 6190283 Japan

ISBN: (纸本)9781424414369

In this paper we propose a domain partitioning RankBoost approach for face recognition. This method uses Local Gabor Binary pattern Histogram (LGBPH) features for face representation, and adopts RankBoost to select the most discriminative features. Unlike the original RankBoost algorithm in [1], weak hypotheses in our method make their predictions based on a partitioning of the similarity domain. Since the domain partitioning approach handles the loss function of a ranking problem directly, it can achieve a higher convergence speed than the original approach. Furthermore, in order to improve the algorithm's generalization ability, we introduce some constraints to the weak classifiers being searched. Experiment results on FERET database show the effectiveness of our approach.

关键词： face recognition RankBoost pattern classification

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A reconfigurable computing framework for multi-scale cellular image processing

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MICROPROCESSORS AND MICROSYSTEMS 2007年第8期31卷 546-563页

作者： Porter, Reid Frigo, Jan Conti, Al Harvey, Neal Kenyon, Garrett Gokhale, Maya Los Alamos Natl Lab Int Space & Response Technol Div Space Data Syst Grp Los Alamos NM 87545 USA Los Alamos Natl Lab Int Space & Response Technol Div Space & Remote Sensing Sci Grp Los Alamos NM 87545 USA Los Alamos Natl Lab Div Phys Biophys Grp Los Alamos NM 87545 USA Los Alamos Natl Lab Adv Comp Grp Los Alamos NM 87545 USA

Cellular computing architectures represent an important class of computation that are characterized by simple processing elements, local interconnect and massive parallelism. These architectures are a good match for many image and video processing applications and can be substantially accelerated with Reconfigurable Computers. We present a flexible software/hardware framework for design, implementation and automatic synthesis of cellular image processing algorithms. The system provides an extremely flexible set of parallel, pipelined and time-multiplexed components which can be tailored through reconfigurable hardware for particular applications. The most novel aspects of our framework include a highly pipelined architecture for multi-scale cellular image processing as well as support for several different pattern recognition applications. In this paper, we will describe the system in detail and present our performance assessments. The system achieved speed-up of at least 100x for computationally expensive sub-problems and 10x for end-to-end applications compared to software implementations. (C) 2006 Elsevier B.V. All rights reserved.

关键词： field programmable gate array reconfigurable computing image processing cellular automata cellular nonlinear network

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Self-adaptive evolutionary algorithm for multispectral remote sensing image clustering

Self-adaptive evolutionary algorithm for multispectral remot...

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5th International Symposium on Multispectral image processing and pattern recognition

作者： Chang, Dongxia Zhang, Xianda Zheng, Changwen Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci Inst Software Natl Key Lab Integrated Informat Syst Technol Beijing 100080 Peoples R China

ISBN: (纸本)9780819469519

In this paper, a self-adaptive evolutionary clustering algorithm is presented. This algorithm uses the evolutionary programming (EP) to search the optimal clustering and bases on the principles of the K-means algorithm. The proposed self-adaptive evolutionary (SAEP) clustering algorithm self-adapts the vector of the step size appropriate for each parent. This is different from other genetic-based algorithms. The algorithm can minimize the degeneracy in the evolutionary process. The experimental results show that the KSAE clustering algorithm is efficient in the unsupervised classification of the multispectral remote sensing image.

关键词： self-adaptive evolutionary programming K-means algorithm multispectral remote sensing image

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Towards the development of next generation remote sensing technology - Erdas imagine incorporates a higher order feature extraction technoque based on ICA

American Society for Photogrammetry and Remote Sensing - ASP...

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American Society for Photogrammetry and remote sensing - ASPRS Annual conference 2007: Identifying Geospatial Solutions 2007年 2卷 666-677页

作者： Shah, Chintan A. Anderson, Ian Gou, Zaiyong Hao, Steven Leason, Andrew University of California Los Angeles Los Angeles CA United States Leica Geosystems Geospatial Imaging Norcross GA United States

ISBN: (纸本)9781604232240

Analysis of multi/hyperspectral imagery necessitates a selection of an optimal subset of bands in order to avoid the Hughes phenomena and parameter estimation problems due to interband correlation. This can be achieved by employing feature extraction techniques for significant reduction of data dimensionality. From the perspective of statistical pattern recognition, feature extraction refers to a process, whereby a data space is transformed into a feature space, in which the original data is represented by a reduced number of effective features, retaining most of the intrinsic information content. Feature extraction from remote sensing imagery must also aim to enhance the discriminability of surface materials based on their spectral characteristics. Conventional remote sensing feature extraction techniques such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) model the multi/hyperspectral image data with a multivariate Gaussian distribution. Inadequacy of such algorithms stems from the Gaussian distribution assumption, which is only an assumption rather than a demonstrable property of most remote sensing data. In order to overcome this fundamental limitation, we at Leica Geosystems have developed a feature extraction technique based on Independent Component Analysis (ICA) that exploits the higher order statistical characteristics of multi/hyperspectral imagery. In this paper, we will provide a theoretical formulation of ICA and prove its superiority over PCA and MNF in a mathematical framework. Our illustrations would also demonstrate the potential benefits of employing ICA in improving the performance of several multi/hyperspectral analysis techniques such as spectral unmixing, classification, and anomaly detection.

关键词： Independent component analysis

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Spatial variability analysis to remote sensing image on different scales with wavelet variance

Spatial variability analysis to remote sensing image on diff...

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5th International Symposium on Multispectral image processing and pattern recognition

作者： Ma, Lingling Tang, Lingli Li, Zhaoliang Chinese Acad Sci Inst Remote Sensing Applicat Beijing 100864 Peoples R China Chinese Acad Sci Remote Sensing Satellite Ground Stn Beijing Peoples R China Chinese Acad Sci Grad Sch Beijing Peoples R China Chinese Acad Sci Acad Opto Elect Beijing Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing Peoples R China

ISBN: (纸本)9780819469502

This paper has chosen two different kinds of sites including farmland and an area with a lake. SPOT5-HRG image (10m spatial resolution) is used to derived NDVI and albedo in these sites. In order to describe the variability for each length scale on particular resolution, we calculate the wavelet variance with Haar wavelet which is concluded to be the best wavelet for calculating length scales. Then we analyze the dominant length scale on which the maximum wavelet variance is calculated. To assess the errors of aggregation, we compare the relative aggregated error on two different sites;the result shows that scaling bias of surface parameters retrieved from remote sensing data has close relationship with the spatial heterogeneity of the pixel, as well as the degree of non-linearity of the transfer function. The spatial variability analysis using wavelet variance can provide reference on designing remote sensing satellite with certain aim.

关键词： wavelet variance relative aggregated error leaf area index validation

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A FCM algorithm for remote-sensing image classification considering spatial relationship and its parallel implementation

A FCM algorithm for remote-sensing image classification cons...

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5th International conference on Wavelet Analysis and pattern recognition

作者： Gong, Xue-Jing Ci, Lin-Lin Yao, Kang-Ze Beijing Inst Technol Beijing 100083 Peoples R China Equipment Inst PLA Second Artilery Beijing 100085 Peoples R China Acad Equipment Command & Technol Beijing 101416 Peoples R China

ISBN: (纸本)9781424410651

Fuzzy C-Means clustering is one of the most perfective and widely, used algorithms based on objective function for unsupervised classification. Considering the spatial relationship of pixels when it is used in remote sensing imagery, Neighbor-based FCM algorithm is put forward with the method of modifying the value of fuzzy membership degrees with the neighbor information during the clustering iterations. We use dominant class, if it can be determined in a fixed neighbor region, or the weighted parameters based 017 the distance of neighbours to perfect the membership degrees of central pixel. Then parallel implement for the algorithim is also proposed by, taking account into the communication complexity and the spatial relationship for image partition. 117 the end, the experimental data indicate the efficiency of the algorithim in decreasing the amount of clustering iterations and increasing the classfied precision;the parallel algorithm also achieves the satisfied linear speedup.

关键词： fuzzy C-Means clustering neighbor region fuzzy membership degree parallel algorithm

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Method of airport detection based on multi-resolution and multi-spectrum remote sensing images - art. no. 67860H

Method of airport detection based on multi-resolution and mu...

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5th International Symposium on Multispectral image processing and pattern recognition

作者： Wang, Cheng Sun, Hai Ren, Bo Huazhong Univ Sci & Technol Digital Engn & Simulat Ctr Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9780819469502

Airport detection is one of the most important research topics in the remote sensing application field. In the field, majority of airport detection methods are designed based on different single resolution optical remote sensing images. However, because of the RS data's limitation and algorithms' shortages, these methods are not likely to have a perfect performance;inaccuracy and inefficiency are the two main drawbacks. In recent years, while much research of airport recognition has been done on single resolution images, less work has been tried to study on multi-resolution and multi-spectrum images together. Nevertheless, after many experiences, the latter method is proved available and attemptable in our research. Based on the different characteristics of multi-resolution and multi-spectrum images, we try to design a synthetical model which includes algorithms of structure feature analysis and spectral analysis. In the model, different advantages of images and methods are combined to reduce influence of each other's limitations. Finally, an airport-detection system is implemented based on the synthesis method. This paper focuses on the designment and implementation of the synthesis model, enrich the research of airport recognition and solve many problems. Section I provides an overview of the synthesis model. Section 2 discusses the core algorithms of the method in detail. Section 3 demonstrates airport detention system and Section 4 concludes the paper.

关键词： airport detection remote sensing images spectral analysis multi-resolution multi-spectrum

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