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

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remotely sensed object recognition using multiple comparative measures

Remotely sensed object recognition using multiple comparativ...

引用

conference on image and Signal processing for remote sensing IX

作者： Power, GJ USAF Res Lab Target Recognit Branch AFRL SNAT Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819451215

In the real world of remote sensing, rarely does the extracted object precisely match a stored template of that object. A certain level of uncertainty must be permitted between the stored template and the extracted object. One solution to deal with this uncertainty is to evaluate measures between the object and template. Many measures have been introduced independently in literature for evaluating the statistical nature of the extracted objects such as a variety of shape and texture measures. The object is measured and compared to similar measures taken from the template. This paper suggests using measures extracted through a joint comparative process of template and object. It also suggests using multiple measures from the joint class of measures as opposed to using an individual measure to determine the sufficiency of the match. In particular, this paper demonstrates the value of using multiple comparative shape measures as opposed to one particular shape measure to achieve confidence in a match. The multiple-shape measure approach uses a matched filter measure, a Procrustes metric, a partial-direct hausdorf measure, and percent-pixels same measure. Each shape measure gives slightly different insight about the shape comparison which allows more confidence in the match. An experimental result is given that demonstrates the implementation and usefulness of the multiple comparative measure approach for recognizing objects from remotely sensed imagery.

关键词： similarity shape object recognition image processing segmentation measures metrics template matching shape analysis image analysis remote sensing feature analysis

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Comparative studies on similarity measures for remote sensing image retrieval

Comparative studies on similarity measures for remote sensin...

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IEEE International conference on Systems, Man and Cybernetics

作者： Bao, Q Guo, P Beijing Normal Univ Dept Comp Sci Beijing 100875 Peoples R China

ISBN: (纸本)0780385667

Similarity measure is usually used to study the similar degree between multi-source data, which is the basis of pattern recognition on spatial data. For it is the core technique in content-based image retrieval., similarity measure has very wide applications. In this paper eight similarity measures are experimental investigated through some remote sensing image retrieval. The features extracted in the experiments are frequency histogram and cumulative histogram vectors. From the experiment results it can be found that chi(2) statistical. distance measure and cosine of the angle measure perform better than others. The results described in this paper are of significance in applications to multi-source data analysis.

关键词： image retrieval similarity measure multi-source data histooram

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Fully rotational invariant recognition of three-dimensional objects

Fully rotational invariant recognition of three-dimensional ...

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conference on Military remote sensing

作者： García, J Valles, JJ Ferreira, C Univ Valencia Fac Fis Dept Opt Burjassot 46100 Valencia Spain

ISBN: (纸本)0819455660

We present a method for coding the information of a 3D object into a representation on a unit sphere. The coding is based on mapping the phase Fourier transform of the range images of the object onto the sphere. This procedure creates a unique object signature that we call three-dimensional object orientation map (3DOOM). The correlation between 3DOOM of different objects is discussed and defined. The maps permit to obtain detection as well as the orientation of an object from a range image, using only partial object information.

关键词： 3D image processing Fourier transforms Optical spheres Spherical lenses image processing Object recognition Computer programming Light sources and illumination Neural networks Phase only filters

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Radar and panchromatic image fusion by means of the a trous algorithm

Radar and panchromatic image fusion by means of the <i>a tro...

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conference on image and Signal processing for remote sensing IX

作者： Chibani, Y Univ Sci & Technol Houari Boumedienne Fac Elect & Informat Algiers 16111 Algeria

ISBN: (纸本)0819451215

Radar imaging provides an important advantage for the earth change observation independently of weather conditions. However. the recognition of some features as roads is more difficult leading thus to an ambiguous interpretation of the scene. In order to compensate the lack of features, the high spatial resolution panchromatic image is often used as a complementary data for improving the quality of the radar image. The basic idea consists to extract features (details) from the panchromatic image by means of the High Pass Filter (HPF) in order to incorporate into the radar image. However, the difficult choice of the size and shape of the filter does not allow enhancing significantly the lack of features into the radar image. To resolve this problem, we propose the use of the a trous algorithm as an alternative approach for extracting features from the panchromatic image. Its advantage lies to the local characterization of features by the wavelet coefficients. The radar-panchromatic composite image produced from the trous algorithm gives a better detection of lines, edges and field boundaries compared to the HPF method.

关键词： remote sensing panchromatic and radar images fusion HPF filter redundant wavelet decomposition a trous algorithm

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An automated object-based classification approach for updating Corine land cover data

An automated object-based classification approach for updati...

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

作者： Wehrmann, T Dech, S Glaser, R DLR DFD German Remote Sensing Data Ctr Wessling Germany

ISBN: (纸本)0819455210

In this paper, the framework of an object-based classification approach for land cover and land use classes is presented. Recently, there is an increasing demand for information on actual land cover resp. land use from planning, administration and science institutions. remote sensing provides timely information products in different geometric and thematic scales. The effort to manually classify land use data is still very high. Therefore a new approach is required to incorperate automated image classification to human image understanding. The proposed approach couples object-based clasification technique - a rather new trend in image classification - with machine learning capacities (Support Vector Classifier) depending on information levels. To ensure spatial and spectral transferability of the classification scheme, the data has to be passed through several generalisation levels. The segmentation generates homogeneous and contiguous image objects. The hierarchical rule type uses direct and derived spectral attributes combined with spatial features and information extracted from the metadata. The identified land cover objects can be converted into the current CORINE classes after classification.

关键词： object-based image classification automatisation CORINE land cover pattern recognition machine learning human image understanding

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Robust automatic classification method for hyperspectral imagery

Robust automatic classification method for hyperspectral ima...

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conference on image and Signal processing for remote sensing IX

作者： Gomez-Chova, L Calpe, J Camps-Valls, G Martín, JD Soria, E Vila, J Alonso-Chorda, L Moreno, J Univ Valencia Dept Elect Engn GPDS E-46003 Valencia Spain

ISBN: (纸本)0819451215

In this paper, we propose a new approach to the classification of hyperspectral images. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions;and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning methods that avoids these drawbacks and automates the classification process. The method is based on the general formulation of the expectation-maximization (EM) algorithm. This method is applied to crop cover recognition of six hyperspectral images from the same area acquired with HyMap spectrometer during the DAISEX99 campaign. For classification purposes, six different classes are considered in this area: corn, wheat, sugar beet, barley, alfalfa, and soil. Classification accuracy results are compared to common methods: ISODATA, Learning Vector Quantization, Gaussian Maximum Likelihood, Expectation-Maximization, and Neural Networks. The good performance confirms the validity of the proposed approach in terms of accuracy and robustness.

关键词： remote sensing hyperspectral imaging crop classification automatic hyperspectral. classification partially supervised classification

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Statistical landscape features for texture classification

Statistical landscape features for texture classification

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Proceedings of the 17th International conference on pattern recognition, ICPR 2004

作者： Xu, Cun Lu Chen, Yan Qiu Dept. of Comp. Sci. and Engineering Ctr. Wave Scattering/Remote Sensing Fudan Univ. Shanghai 200433 China

This paper proposes the use of information derived from the graph of a texture image function for texture description. The graph of an image function is a rumpled surface in the three-dimensional space that appears like a landscape. Four novel texture feature curves are used to characterize the texture. This method is named Statistical Landscape Features (SLF). SLF achieves a very high correct classification rate of 94.53% on the entire Brodatz set. Besides the very good performance, another remarkable advantage of the proposed method is that it has no parameter to tune.

关键词： Feature extraction

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Classification of high spatial resolution images by means of a Gabor wavelet decomposition and a support vector machine

Classification of high spatial resolution images by means of...

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conference on image and Signal processing for remote sensing X

作者： Baraldi, A Bruzzone, L ISSIA CNR Bari Italy

ISBN: (纸本)0819455202

Very high spatial resolution satellite images, acquired by third-generation commercial remote sensing (RS) satellites (like Ikonos and QuickBird), are characterized by a tremendous spatial complexity, i.e. surface objects are described by a combination of spectral, textural and shape information. Potentially capable of dealing with the spatial complexity of such images, context-sensitive data mapping systems, e.g. employing filter sets designed for texture feature analysis/synthesis, have been extensively studied in pattern recognition literature in recent years. In this work, four implementations of a two-stage classification scheme for the analysis of high spatial resolution images are compared. Competing first stage (feature extraction) implementations of increasing complexity are: 1) a standard multi-scale dyadic Gaussian pyramid image decomposition, and 2) an original almost complete (near-orthogonal) basis for the Gabor wavelet transform of an input image at selected spatial frequencies (i.e. band-pass filter central frequency and filter orientation pairs). The second stage of the classification scheme consists of. a) an ensemble of pixel-based two-class support vector machines (SVMs) applied to the multi-class classification problem according to the one-against-one strategy, exploiting the well-known SVM's capability of dealing with high dimensional mapping problems;and b) a traditional two-phase supervised learning pixel-based Radial Basis Function (RBF) network. In a badly-posed Ikonos image classification experiment, SVM combined with the two filter sets provide an interesting compromise between ease of use (i.e. easy free parameter selection), classification accuracy, robustness to changes in surface properties, capability of detecting genuine, but small, image details as well as linear structures. Qualitatively and quantitatively, the multi-scale multi-orientation almost complete Gabor wavelet transform appears superior to the dyadic multi-scale Gaussian

关键词： Gabor filter bank Gaussian/Laplacian image decomposition high spatial resolution satellite imagery Hughes phenomenon image classification wavelet

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Comparison of the binary phase only filter design by DBS algorithm with the classical SDF filter

Comparison of the binary phase only filter design by DBS alg...

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conference on Military remote sensing

作者： Butt, JA Wilkinson, TD Univ Cambridge Dept Engn Photon & Sensors Grp Cambridge CB2 1PZ England

ISBN: (纸本)0819455660

The joint transform correlator (JTC) is one of two main optical image processing architectures which provide us with a highly effective way of comparing images in a wide range of applications. Traditionally an optical correlator is used to compare an unknown input scene with a pre-captured reference image library, to detect if the reference occurs within the input. There is a new class of application for the JTC where they are used as image comparators, not having a known reference image, rather frames from a video sequence form both the input and reference. The JTC input plane is formed by combining the current frame with the previous frame in a video sequence and if the frames match, then there will be a correlation peak. If the objects move then the peaks will move (tracking) and if something has changed dramatically in the scene, then the correlation between the two frames is lost. This forms the basis of a very powerful application for the JTC in Defense and Security. Any change in the scene can be recorded and with the inherent shift invariance property of the correlator, any movement of the objects in the scene can also be detected. A major limitation of the JTC is its intolerance to rotation and scale changes in images. The strength of the Correlation signal decreases as the input object rotates or varies in scale relative to the reference object. We have designed a binary phase only filter using the direct binary search algorithm for rotation invariant pattern recognition to be implemented on a JTC and compared to a classical synthetic discriminant function (SDF) filter. Results show that the performance of the DBS filter is better than the SDF filter.

关键词： image filtering Optical correlators Binary data Spatial light modulators Optical filters Phase only filters Virtual colonoscopy Fourier transforms image processing Video

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Features extraction and image analysis of seismic sections

Features extraction and image analysis of seismic sections

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

作者： Zoran, M Zoran, LF Natl Inst R&D Optoelect Remote Sensing Dept Bucharest 76900 Romania

ISBN: (纸本)0819451223

A seismic section is intermediate step between the material response of the Earth surface to seismic activity. As derived from materials properties (density, elastic modules and attenuation) the seismic response is numerical in nature. Data sets are transformed into plots or are exibited as images. Geological interpreting of cross section is based on regions of lithological affinity. An interpreter considers the section as a transitional step from a wavefield to a map and views it as an image. Understanding the section is a complex process of features extraction and image analysis. Seismic sections contain 10(5) times more data that is needed to fully describe a completely interpreted section. This paper suggests three ways to reduce the data set size for seismic section analysis by a global factor of 100 or more The interpretation becomes a problem of a pattern classification using clustering algorithms that highlight the important stratigraphic attributes by operating on various transformed domains. The evaluation of seismic sections means processing the data and the new image interpreting. Could be identified :location and dip of faults, sense of motion along them, boundaries, reflection geometry and the degree of continuity. Such model can be applied for seismic sections in Vrancea seismic area,Romania.

关键词： image analysis seismic section geologic faults Vrancea Romania

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