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conference on Computer Vision and pattern recognition (CVPR)

作者： Huawu Deng D.A. Clausi Systems Design Engineering University of Waterloo Canada

The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. The paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modeling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.

关键词： remote sensing Least squares approximation image texture Discrete Fourier transforms Markov random fields Anisotropic magnetoresistance image retrieval Parameter estimation Synthetic aperture radar Sea ice

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基于方向梯度局部方差的分层遥感图像融合

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数据采集与处理 2003年第4期18卷 369-372页

作者：强赞霞彭嘉雄王洪群华中科技大学图像识别与人工智能研究所图像信息处理与智能控制教育部重点实验室

为了充分利用各种遥感图像的信息 ,本文对以往图像融合技术进行了简要的概述 ,分析了各种方法的优缺点 ,给出了一种新的基于方向梯度局部方差的金字塔图像融合方法。将各源图像进行金字塔分解 ,得到多分辨率描述 ,建立方向梯度金字塔 ,... 详细信息

为了充分利用各种遥感图像的信息 ,本文对以往图像融合技术进行了简要的概述 ,分析了各种方法的优缺点 ,给出了一种新的基于方向梯度局部方差的金字塔图像融合方法。将各源图像进行金字塔分解 ,得到多分辨率描述 ,建立方向梯度金字塔 ,采用局部方差准则融合源图像的各分解层 ,再进行金字塔分解的逆变换 ,得到融合图像。此外 ,利用信息熵等准则和其他融合方法进行比较。

关键词：分层遥感图像图像融合方向梯度局部方差图像处理计算机

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An unsupervised approach to color video thresholding

An unsupervised approach to color video thresholding

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IEEE International conference on Multimedia and Expo (ICME)

作者： E.Y. Du Chein-I Chang P.D. Thouin Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD Department of Defense Fort Meade MD USA

Thresholding color video images is challenging because of the low spatial resolution and the complex backgrounds. This paper investigates the issue of thresholding these images by reducing the number of colors in order to improve automated text detection and recognition. An unsupervised thresholding approach is presented which reduces the background complexity while retaining the important text character pixels. The experiments show that our proposed thresholding approach performs significantly better than simple image histogram-based methods, which generally do not produce satisfactory results.

关键词： Color image segmentation Information retrieval image retrieval image resolution Fuses Gray-scale remote sensing Signal processing image processing

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A unified unsupervised clustering algorithm and its first application to landcover classification

A unified unsupervised clustering algorithm and its first ap...

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

作者： Y. Yu I. Bloch A. Trouve Département TSI CNRS URA 820 Ecole Nationale Supérieure des TéIécommunications Paris France LAGA CNRS UMR 7539 Institut Galilee Université Paris 13 Villetaneuse France

The problem of classification is so fundamental that it has been intensively investigated by many researchers from different domains. In this paper, we present a novel unsupervised clustering algorithm derived from the techniques of probabilistic modeling which is implemented by a stochastic gradient algorithm. Then its application to challenging landcover classification based on Daedalus data of the SMART project is explored by combining both spectral feature and spatial contextual information. Our first experiments show its potential usefulness in remote sensing.

关键词： Clustering algorithms Classification algorithms Clustering methods Optimization methods remote sensing pattern recognition Statistics Psychology Information retrieval image retrieval

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Spectral fuzzy classification system for target recognition

Spectral fuzzy classification system for target recognition

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conference of the North American Fuzzy Information processing Society - NAFIPS

作者： A. Del Amo D. Gomez J. Montero Electronics Systems-Grand Rapids Smiths Aerospace MI USA Department Statistics and O R. Complutense University of Madrid Madrid Spain

The goal of this paper is to present an algorithm for terrain matching, leveraging an existing fuzzy clustering algorithm, and modifying it to its supervised version, in order to apply the algorithm to georegistration and, later on pattern recognition. Georegistration is the process of adjusting one drawing or image to the geographic location of a "known good" reference drawing, image, surface or map, The georegistration problem can be treated as a pattern recognition problem; and it can be applied to the target detection problem. The terrain matching algorithm will be based on fuzzy set theory as a very accurate method to represent the imprecision of the real world, and presented as a multicriteria decision making problem. The energy emitted and reflected by the Earth's surface has to be recorded by relatively complex remote sensing devices that have spatial, spectral and geometrical resolution constraints. Errors usually slip into the data acquisition process. Therefore, it is necessary to preprocess the remotely sensed data, prior to analyzing it (image restoration, involving the correction of distortion, degradation and noise introduced during the rendering process). In this paper we shall assume that all these problems have been solved, focusing our study on the image classification of a corrected image being close enough, both geometrically and radiometrically, to the radiant energy characteristics of the target scene. In particular, at a first stage we consider each pixel individually; and a class will be assigned to each pixel, taking into account several values measured in separate spectral bands. Then we shall describe an automatic detection system based on a previous algorithm developed in A. Del Amo et al., introducing now the fuzzy partition model proposed by A. Del Amo et al. A first phase will lead to a spectral definition of patterns; and a second phase will lead to classification and recognition. Similarity measures will then allow us to evaluate th

关键词： Fuzzy systems Target recognition Clustering algorithms pattern recognition pattern matching Surface treatment Object detection Fuzzy set theory Decision making Earth

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Flexible simulation strategy for modeling 3D cultural objects based on multi-source remotely sensed imagery

Flexible simulation strategy for modeling 3D cultural object...

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conference on Videometrics VII

作者： Guienko, G Levin, E Siberian State Geodet Acad Dept Photogrammetry & Remote Sensing Novosibirsk 630107 Russia

ISBN: (纸本)0819448133

New ideas and solutions never come alone. Although automated feature extraction is not sufficiently mature to move from the realm of scientific investigation into the category of production technology, a new goal has arisen: 3D simulation of real-world objects, extracted from images. This task, which evolved from feature extraction and is not an easy task itself, becomes even more complex, multi-leveled, and often uncertain and fuzzy when one exploits time-sequenced multi-source remotely sensed visual data. The basic components of the process are familiar image processing tasks: fusion of various types of imagery, automatic recognition of objects, removing those objects from the source images, and replacing them in the images with their realistic simulated "twins"-object rendering. This paper discusses how to aggregate the most appropriate approach to each task into one technological process in order to develop a Manipulator for Visual Simulation of 3D objects (ManVIS) that is independent or imagery/format/media. The technology could be made general by combining a number of competent special purpose algorithms under appropriate contextual, geometric, spatial, and temporal constraints derived from a-priori knowledge. This could be achieved by planning the simulation in an Open Structure Simulation Strategy Manager (O3SM)-a distinct component of ManVIS-building the simulation strategy before beginning actual image manipulation.

关键词： visual data fusion feature extraction 3D simulation remotely sensed imagery

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Study of artificial neural network method used for weather and AVHRR thermal data classification

Study of artificial neural network method used for weather a...

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Third International Symposium on Multispectral image processing and pattern recognition

作者： Bagan, Hasi Ma, Jianwen Zhou, Zijiang Inst. of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Climate Date Center National Metorological-Center CMA Beijing 100081 China

In resent years, the Asian dust storm project was carried out. One of tasks was to study dust rising mechanism in dust source area. Surface temperature condition was regarded as one of important factors for dust rise. In the study we retrieved surface temperature by using NOAA/AVHRR data. Based on published articles, traditionally, split window algorithm was use to deriving surface temperatures in the case of our study area mostly desert area, there was only three field observation data available in Talimu basin, at Dunhuang and Changwu. It was very difficult to validate the results. However, There were 52 county weather observation stations in the area. The data might be used as import data in artificial neural network calculation. Most success examples of remote sensing data classification by using neural network were in the condition of network training and classifying in the same types of data such as spatial data. For the use different data type collected by different techniques system such as satellite system and ground weather observation data to training, to find rule and to direct classification could be more impersonal which was one of the nature of artificial neural network method. In our case 52 weather temperature data were used from 52 observation stations where they were also the same positions for collecting AVHRR 1b data CH3, CH4, CH5 thermal data. Both groups of data were applied as fundamental import data in for artificial neural network calculation. Finally resultant rule was applied for classifying 15000 × 3 pixels in the whole area. The result was more reliable than that of split window not only because uncertainty caused by variations of topography but also it was very difficult to validate in field.

关键词： Neural networks

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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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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： L. Gomez-Chova J. Calpe G. Camps-Valls J.D. Martin E. Soria J. Vila L. Alonso-Chorda J. Moreno Department of Electronic Engineering (GPDS) & Department of Thermodynamics University of 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 of 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 the 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 on order to gain knowledge on the problem and to give a physical interpretation of the results.

关键词： Hyperspectral imaging Crops Hyperspectral sensors pattern recognition Spectroscopy image processing Extraterrestrial phenomena remote sensing Earth Composite materials

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A fusion algorithm of remote sensing image based on discrete wavelet packet

A fusion algorithm of remote sensing image based on discrete...

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International conference on Machine Learning and Cybernetics (ICMLC)

作者： Hai-Hui Wang Jia-Xiong Peng Wei Wu State Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780378652

image fusion refers to the techniques that integrate complementary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and the compute processing tasks. In this paper, a new image fusion algorithm based on wavelet packet transform to fuse multisensor images is presented. Discrete wavelet transform (DWT) can offer a more precise way for image analysis, than other multi-resolution analysis. It decomposes an image into low frequency band and high frequency band in different level, and it can also be reconstructed gradually in different level. But this method only decomposes low frequency band in a higher scale, so that it omits some useful details of the images. In this paper, we present a new image fusion algorithm. In the algorithm, we use discrete wavelet packet transform (DWPT) to decompose and reconstruct the images. When images are merged in wavelet packet space, different frequency ranges are processed differently. It can merge information from original images adequately and improve abilities of information analysis and feature extraction. This image fusion is performed at the pixel level. In this fusion algorithm, a feature-based fusion rule is used to combine original subimages and to form a pyramid for the fused image. Through merging remote sensing images from multi-sensor to a same object by applying method of wavelet packet analysis, we have obtained a fused picture. In this paper, mutual information is employed as a means of objective assessing image fusion performance. The experiment results show that this fusion algorithm, based on wavelet packet transform, is an effective approach in image fusion area.

关键词： remote sensing Discrete wavelet transforms Wavelet packets image fusion Frequency image analysis Wavelet analysis image reconstruction image sensors Humans

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Multisensor image fusion and mining: from neural systems to COTS software with application to remote sensing AFE

Multisensor image fusion and mining: from neural systems to ...

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IEEE Workshop on Advances in Techniques for Analysis of remotely Sensed Data

作者： M. Chiarella D.A. Fay A.M. Waxman R.T. Ivey N. Bomberger Fusion Technology Systems Division Cognitive Fusion Technology Directorate Alpha Tech Inc. Burlington MA USA

We summarize our methods for the fusion of multisensor/spectral imagery based on concepts derived from neural models of visual processing (adaptive contrast enhancement, opponent-color contrast, multi-scale contour completion, and multi-scale texture enhancement) and semi-supervised pattern learning and recognition. These methods have been applied to the problem of aided feature extraction (AFE) from remote sensing airborne multispectral and hyperspectral imaging systems, and space-based multi-platform multi-modality imaging sensors. The methods enable color fused 3D visualization, as well as interactive exploitation and data mining in the form of human-guided machine learning and search for objects, landcover, and cultural features. This technology has been evaluated on space-based imagery for the National imagery and Mapping Agency, and real-time implementation has also been demonstrated for terrestrial fused-color night imaging. We have recently incorporated these methods into a commercial software platform (ERDAS Imagine) for imagery exploitation. We describe the approach and user interfaces, and show results for a variety of sensor systems with application to remote sensing feature extraction including EO/IR/MSI/SAR imagery from Landsat and Radarsat, multispectral Ikonos imagery, and Hyperion and HyMap hyperspectral imagery.

关键词： image fusion Software systems Application software remote sensing Hyperspectral imaging Feature extraction Hyperspectral sensors Sensor systems pattern recognition image recognition

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