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conference on image processing and pattern recognition in remote sensing

作者： Zhao, Y Chen, YQ Fudan Univ Dept Comp Sci & Engn Shanghai 200433 Peoples R China

ISBN: (纸本)081944684X

This paper deals with the registration of images which are subject to translation, rotation, scaling and other geometric transformations. The problem is tackled with a contour-based registration technique. We present a novel approach for finding a representation of planar curves and matching such two representations in this paper. In our framework, the matching of curves is proceeded by 1) using connected equi-length line segments (CELLS) to represent curves, (2) attaching an identification vector to each line segment, which reflects the distribution of the rest of line segments with respect to the current one using orientation difference between cells. A new matrix called Orientation Difference Matrix (ODM) has been constructed from the identification vectors. This approach uniquely specifies a curve and the representation for matching is invariant under rotation, scaling and translation of the curve. A practical use of the proposed approach is demonstrated by registering a SAR image of a certain area to a map.

关键词： registration connected equi-length line segment identification vector SAR image orientation difference matrix

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Automatic flat field algorithm for hyperspectral image calibration

Automatic flat field algorithm for hyperspectral image calib...

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3rd International Symposium on Multispectral image processing and pattern recognition

作者： Zhang, X Zhang, B Geng, XR Tong, QX Zheng, LF Acad Sinica Inst Remote Sensing Applicat Lab Remote Sensing Informat Beijing 100101 Peoples R China

ISBN: (纸本)0819451819

image spectra calibration is of great importance for further processing, and feature extraction. In this paper, an automated flat field reflectance calibration algorithm (AFFT) is proposed. This algorithm is an improvement to the traditional flat field transformation calibration. It is based on the fact that the so-called flat field is a flat block of high brightness and relative flat spectral response, and at a certain wavelength range (e.g. 500-700nm) the brightness or radiance of the flat field is a certain Multiple of the average spectrum of the image. Because the average image spectrum usually is relatively flat, so a certain multiple of the average spectrum can be regarded as the criterion (or threshold) to select flat field pixels. So such parameters as wavelength range, multiple increment between flat field and the average image spectrum and number of the largest area block are set to determine the useful flat field so that an average spectrum of the flat field is obtained. By using this flat field spectrum as solar/atmospheric response, hyperspectral image can be calibrated to reflectance image. In the end, AFFT was validated by one PHI image acquired in Japan, 2000. It turns out that AFFT is effective to search all the flat fields which meet the fixed terms automatically and promptly, the spectra transformed by this method are much smoother and reliable to some extent.

关键词： hyperspectral image automatic flat field average image spectrum reflectance image

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Watershed-based image Segmentation with Region Merging and Edge Detection

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High Technology Letters 2003年第1期9卷 58-63页

作者： Salman N H Institute of Image Processing Pattern Recognition Shanghai Jiaotong University Shanghai P. R. China 200030

The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A watershed transformation technique is then employes. This includes: gradient of the classified image, dividing the image into markers, checking the Marker image to see if it has zero points (watershed lines). The watershed lines are then deleted in the Marker image created by watershed algorithm. A Region Adjacency Graph (RAG) and Region Adjacency Boundary (RAB) are created between two regions from Marker image. Finally region merging is done according to region average intensity and two edge strengths (T1, T2). The approach of the authors is tested on remote sensing and brain MR medical images. The final segmentation result is one closed boundary per actual region in the image.

关键词： image segmentation edge detection watershed K-means edge strength brain images remote sensing images region adjacency graph (RAG).

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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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Morphological theory in image feature extraction

Morphological theory in image feature extraction

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image processing and pattern recognition in remote sensing

作者： Gui, Feng QiWei, Lin Jianghan Petroleum University Dept. of Electronics Hubei Jingzhou 434023 China

As we known that morphology is the technique that based upon set theory and it can be used for binary image processing and gray image processing. The principle and the geometrical meaning of morphological boundary detecting for image were discussed in this paper, and the selecting of structure element was analyzed. Comparison was made between morphological boundary detecting and traditional boundary detecting method, conclusion that morphological boundary detecting method has better compatibility and anti-interference capability was reached. The method was also used for L.V. cineangiograms processing. In this paper we hoped to build up a foundation for automatic detection of L.V. contours based on the features of L.V. cineangiograms and Morphological theory, for the further study of L.V. wall motion abnormalities, because wall motion abnormalities of L.V. due to myocardia ischeamia caused by coronary atherosclerosis is a significant feature of Atherosclerotic coronary heart disease (CHD). An algorithm that based on morphology for L.V. contours extracting was developed in this paper.

关键词： Feature extraction

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Change detection and update of digital orthoimage based on GIS

Change detection and update of digital orthoimage based on G...

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3rd International Symposium on Multispectral image processing and pattern recognition

作者： Zhang, JQ Su, GZ Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)0819451819

Based on analysis of many image change detection methods, a new method of multidimensional change template analysis is proposed in this paper. The new method combines the use of GIS knowledge and the advantages of other change detection methods. It has been used to detect change and update image database for the different temporal digital orthophoto maps (DOM). Test results show that this method is effective.

关键词： orthoimage changing detection update detection template image matching gray level coocurrence matrix GIS quadtree changing detection template

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United moment invariants for shape discrimination

United moment invariants for shape discrimination

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IEEE International conference on Robotics, Intelligent Systems and Signal processing

作者： Sun, YN Liu, WJ Wang, YC Chinese Acad Sci Grad Sch Adv Mfg Lab Shenyang 110016 Peoples R China

ISBN: (纸本)078037925X

Shape discrimination occupied a very important place in pattern recognition, image manipulation. However because of the affection of many factors, the geometric varieties of shape will be happened. It is needed to find the invariant feature values in all kind of instances. So, this paper proposes some new formulas of moment invariants that are defined united moments. The rotation, translation and scaling can be discretely kept invariant to region, closed and unclosed boundary. And the experiments show the effectivity of the united moments.

关键词： pattern recognition boundary-value problems image processing moment invariants shape discrimination pattern recognition image manipulation

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Subsurface detection of coral reefs in shallow waters using hyperspectral data

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

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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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City Modeling by videogrammetry based on three video image sequences strips taken from helicopter

City Modeling by videogrammetry based on three video image s...

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3rd International Symposium on Multispectral image processing and pattern recognition

作者： Wu, J Zhang, ZX Zhang, JQ Wuhan Univ Coll Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)0819451819

Interest in 3D city Modeling and quickly updating have significantly raised in the past years. In this paper, an economically viable solution for city modeling based on strict digital photogrammetric theory is proposed. Two oblique image sequences for buildings' walls and one vertical image sequence for building's roof, acquired by digital video camera on helicopter, coarse 2D vector data of buildings and LIDAR data are used as sources of information. Camera parameters of each image are acquired by automatic aerial triangulation technique for high overlapping image sequence. By matching every space edge of the building to its image line, the building is accurately reconstructed. The visualization of 3D city model is realized based on the reconstructed buildings with fine textures superimposed on DSM and orthoimage.

关键词： city modeling videogrammetry image sequence automatic aerial triangulation texture mapping

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High dimensional multispectral image fusion: classification by neural network

High dimensional multispectral image fusion: classification ...

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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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