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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

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Spectral fuzzy classification system for target recognition 22

Spectral fuzzy classification system for target recognition

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22nd International conference of the North-American-Fuzzy-Information-processing-Society (NAFIPS)

作者： Del Amo, A Gómez, D Montero, J Smiths Aerosp Elect Syst Grand Rapids MI USA

ISBN: (纸本)0780379187

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. [3, S], introducing now the fuzzy partition model proposed by A. Del Amo et al. [2, 4]. 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

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

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Subsurface detection of coral reefs in shallow waters using hyperspectral data

Subsurface detection of coral reefs in shallow waters using ...

引用

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

作者： Rodríguez-Díaz, E Jiménez-Rodríguez, LO Vélez-Reyes, M Gilbes, F DiMarzio, C Univ Puerto Rico ECE Dept Lab Appl Remote Sensing & Imaging Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819449539

Hyperspectral remote sensing has the potential to be used as an effective coral monitoring system from either space or airborne sensors. The problems to be addressed in hyperspectral imagery of coastal waters are related to the medium, which presents high scattering and absorption, and the object to be detected. The object to be detected, in this case coral reefs or different types of ocean floor, has a weak signal as a consequence of its interaction with the medium. The retrieval of information about these targets requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. This paper presents the development of algorithms that does not use labeled samples to detect coral reefs under coastal shallow waters. Synthetic data was generated to simulate data gathered using a high resolution imaging spectrometer (hyperspectral) sensor. A semi-analytic model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. Tikhonov method of regularization was used as a starting point in order to arrive at an inverse formulation that incorporates a priori information about the target. This expression will be used in an inversion process on a pixel by pixel basis to estimate the ocean floor signal. The a priori information is in the form of previously measured spectral signatures of objects of interest, such as sand, corals, and sea grass.

关键词： remote sensing pattern recognition inverse methods estimation theory regularization hyperspectral data image reconstruction image processing classification shallow waters

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High dimensional multispectral image fusion: classification by neural network

High dimensional multispectral image fusion: classification ...

引用

conference on image processing and pattern recognition in remote sensing

作者： He, MY Xia, JT Northwestern Polytech Univ Dept Elect Engn Xian 710072 Peoples R China

ISBN: (纸本)081944684X

Advances in sensor technology for Earth observation make it possible to collect multispectral data in much higher dimensionality. Such high dimensional data will it possible to classify more classes. However, it will also have several impacts on processing technology. First, because of its huge data, more processing power will be needed to process such high dimensional data. Second, because of its high dimensionality and the limited training samples, it is very difficult for Bayes method to estimate the parameters accurately. So the classification accuracy cannot be high enough. Neural Network is an intelligent signal processing method. MLFNN (Multi-Layer Feedforward Neural Network) directly learn from training samples and the probability model needs not to be estimated, the classification may be conducted through neural network fusion of multispectral images. The latent information about different classes can be extracted from training samples by MLFNN. However, because of the huge data and high dimensionality, MLFNN will face some serious difficulties: (1) There are many local minimal points in the error surface of MLFNN;(2) Over-fitting phenomena. These two difficulties depress the classification accuracy and generalization performance of MLFNN. In order to overcome these difficulties, the author proposed DPFNN (Double Parallel Feedforward Neural Networks) used to classify the high dimensional multispectral images. The model and learning algorithm of DPFNN with strong generalization performance are proposed, with emphases on the regularization of output weights and improvement of the generalization performance of DPFNN. As DPFNN is composed of MLFNN and SLFNN (Single-Layer Feedforward Neural Network), it has the advantages of MLFNN and SLFNN: (1) Good nonlinear mapping capability;(2) High learning speed for linear-like problem. Experimental results with generated data, 64-band practical multispectral images and 220-band multispectral images show that the new algo

关键词： multispectral images classification neural network image processing image fusion

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Pixel-based image fusion with false color mapping

Pixel-based image fusion with false color mapping

引用

conference on image processing and pattern recognition in remote sensing

作者： Zhao, W Mao, SY Beijing Univ Aeronaut & Astronaut Dept Elect Engn Fac 203 Beijing 100083 Peoples R China

ISBN: (纸本)081944684X

In this paper, we propose a pixel-based image fusion algorithm that combines the gray-level image fusion method with the false color mapping. This algorithm integrates two gray-level images presenting different sensor modalities or at different frequencies and produces a fused false-color image. The resulting image has higher information content than each of the original images. The objects in the fused color image are easy to be recognized. This algorithm has three steps: first, obtaining the fused gray-level image of two original images;second, giving the generalized high-boost filtering images between fused gray-level image and two source images respectively;third, generating the fused false-color image. We use the hybrid averaging and selection fusion method to obtain the fused gray-level image. The fused gray-level image will provide better details than two original images and reduce noise at the same time. But the fused gray-level image can't contain all detail information in two source images. At the same time, the details in gray-level image cannot be discerned as easy as in a color image. So a color fused image is necessary. In order to create color variation and enhance details in the final fusion image, we produce three generalized high-boost filtering images. These three images are displayed through red, green and blue channel respectively. A fused color image is produced finally. This method is used to fuse two SAR images acquired on the San Francisco area (California, USA). The result shows that fused false-color image enhances the visibility of certain details. The resolution of the final false-color image is the same as the resolution of the input images.

关键词： image fusion data fusion multisensor fusion false color

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A geographical information system for flight safety

A geographical information system for flight safety

引用

2nd conference on remote sensing for Environmental Monitoring, GIS Applications and Geology

作者： Yamamoto, H Homma, K Gomi, H Kitagata, S Kumasaka, K Oikawa, T Natl Aerosp Lab Flight Syst Res Ctr Mitaka Tokyo 1810015 Japan

ISBN: (纸本)0819446688

This paper proposes a geographical information system for terrain and obstacle awareness and alerting that extracts information from high-resolution satellite images. On-board terrain elevation databases are being increasingly used in aircraft terrain awareness and warning systems (TAWS), offering a step change in capability from the radar altimeter-based ground proximity warning system. However, to enhance the safety of flight of small aircraft and helicopters, in addition to pure topographic information a TAWS database should also contain significant man-made obstacles that present a collision hazard, such tall buildings and chimneys, communications masts and electrical power transmission lines. Another issue is keeping the terrain and obstacle database current, reflecting changes to features over time. High-resolution stereoscopic images remotely sensed from Earth orbit have great potential for addressing these issues. In this paper, some critical items are discussed and effective information processing schemes for extracting information relevant to flight safety from satellite images are proposed.

关键词： stereo image flight safety flight obstacle satellite image image recognition geographical information system

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Contour-based shape representation for image compression and analysis

引用

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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A remote sensing image classification method based on evidence theory and neural networks

A remote sensing image classification method based on eviden...

引用

International conference on Neural Networks and Signal processing

作者： Tianqiang Peng Bicheng Li Huan Su Department of Information Science Information Engineering Institute Information and Engineering University Zhengzhou Henan China

ISBN: (纸本)0780377028

Neural networks have been widely used in remote sensing image classification. In this paper, we exploited the spatial information of the image to decide the classification result and proposed a remote sensing image classification method based on D-S evidence theory and neural networks. First, the original image to be classified is smoothed with the smoothed image obtained. Next, a B-P neural network is used to train and classify the original image and its smoothed image separately. Next, the two classification results (decisions) of the B-P neural network are fused with evidence theory. Finally, the fused result is as the final classification result of the original image. Experiment results show that the new method is efficient and improves the classification accuracy largely.

关键词： remote sensing image classification Neural networks Pixel Feedforward neural networks pattern recognition Artificial neural networks Information science image recognition Data mining

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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...

引用

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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Wavelet packet image coder using coefficients partitioning for remote sensing images

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1st Iberian conference on pattern recognition and image Analysis, IbPRIA 2003

作者： Han, Su-Young Cho, Seong-Yun Dept. of Electronic Engineering Hanyang University 17 Haengdang-dong Seongdong-Gu Seoul133-791 Korea Republic of Dept. of Digital Media Anyang University Anyang 5-dong Manan-gu AnyangKyonggi-do708-113 Korea Republic of

ISBN: (纸本)3540402179

In this paper, a new embedded wavelet packet image coder algorithm is proposed for an effective image coder using correlation between partitioned coefficients. This new algorithm presents parent-child relationship for reducing image reconstruction error using relations between individual frequency sub-bands. By parent-child relationships, every coefficient is partitioned and encoded for the zerotree data structure. It is shown that the proposed wavelet packet image coder algorithm achieves lower bit rates than SPIHT. It also demonstrates higher PSNR under the same bit rate. The perfect rate control is compared with the conventional methods. These results show that the encoding and decoding processes of the proposed coder are simpler and more accurate than the conventional ones for texture images that include many mid and high-frequency elements such as aerial and satellite photograph images. The experimental results imply the possibility that the proposed method can be applied to real-time vision system, on-line image processing and image fusion which require smaller file size and better resolution. © Springer-Verlag Berlin Heidelberg 2003.

关键词： image compression

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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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