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ISAR imaging based on Sparse subbands fusion 9

ISAR imaging based on Sparse subbands fusion

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9th International Symposium on multispectral image Processing and Pattern recognition (mippr) - multispectral image acquisition, Processing, and analysis

作者： Li, Gang Tian, Biao Xu, Shiyou Chen, Zengping Natl Univ Def Technol Sci & Technol Automat Target Recognit Lab ATR Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781510600522

Data fusion using subbands, which can obtain a higher range resolution without altering the bandwidth, hardware, and sampling rate of the radar system, has attracted more and more attention in recent years. A method of ISAR imaging based on subbands fusion and high precision parameter estimation of geometrical theory of diffraction (GTD) model is presented in this paper. To resolve the incoherence problem in subbands data, a coherent processing method is adopted. Based on an all-pole model, the phase difference of pole and scattering coefficient between each sub-band is used to effectively estimate the incoherent components. After coherent processing, the high and low frequency sub-band data can be expressed as a uniform all-pole model. The gapped-data amplitude and phase estimation (GAPES) algorithm is used to fill up the gapped band. Finally, fusion data is gained by high precision parameter estimation of GTD-all-pole model with full-band data, such as scattering center number, scattering center type and amplitude. The experimental results of simulated data show the validity of the algorithm.

关键词： ISAR imaging subbands geometrical theory of diffraction (GTD) parameter estimation

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Level Set method in standing tree image segmentation based on particle swarm optimization

Level Set method in standing tree image segmentation based o...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Kan, Jiangming Li, Hongjun Li, Wenbin Beijing Forestry Univ Sch Technol 35 Qing Hua East RdHai-Dian Dist Beijing 100083 Peoples R China Beijing Forestry Univ Coll Sci Beijing 100083 Peoples R China

ISBN: (纸本)9780819469502

For the intelligent pruning machine, a machine vision system is pre-requisite. Standing tree image segmentation is a key step for the machine vision system. An efficient scheme for tree image segmentation was proposed according to the need of the machine vision system of the intelligent pruning machine. The scheme is a level set method based on particle swarm optimization. According to principal of the level set method, the image segmentation is formulated as one of optimization problems. The energy function is taken as the segmentation quality criteria, which consists of an internal energy term that penalizes the deviation of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature, such as object boundaries. In this paper, the method used particle swarm optimization to solve the optimization problems that is different from the ordinary level set method that uses the partial differential equation method in some literatures. In experiments, tree images with different background are selected to test the efficiency of the scheme that presented in this paper. In order to test the antimonies performance of the scheme that presented in this paper, a tree image added Gaussian white noise is selected. From the results of the tree image segmentation, the scheme that presented in this paper is more efficiently. The experimental results demonstrate the scheme is more effective and time-saving than the ordinary level set method.

关键词： level set image segmentation standing tree pruning machine particle swarm optimization

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analysis on hyperspectral target detection ability and target feature in different spatial resolution

Analysis on hyperspectral target detection ability and targe...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Luo, Wenfei Zhong, Liang Zhang, Bing Luo, Shoumei Chinese Acad Sci Inst Remote Sensing Applicat Beijing 100101 Peoples R China South China Normal Univ Sch Geog Sci Guangzhou 510631 Guangdong Peoples R China

ISBN: (纸本)9780819469519

Pattern recognition has been successfully applied to target detection. The characteristic of target pattern determines the detection ability in pattern recognition. The pattern of spectral signature at hyperspectral resolution provides more distinguished spectral feature for target detection and then improves the detection ability. However, hyperspectral image has limitation on low resolution in spatial. Therefore, this article focus on analyze on the target detection ability at the sub-pixel scale in different spatial resolution, considering two critical factors, i.e. spatial response in sensor and background interferer. Experiment data is simulated by inducing these two factors. target-to-Clutter-Ratio (TCR2) curve and Receiver Operating Characteristic (ROC) curve with Uniform target Detector (UTD) analyze on the simulated data. We conclude that spatial response of the sensor and the background interferer induce uncertainty into target detection ability and usually weakens it. It gives rise to a new requirement for hyperspectral target detection that should be considerate for the effect caused by spatial resolution.

关键词： hyperspectral target detection spatial resolution

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analysis of the tropical forest vegetation change in Xishuangbanna of P. R. China by using LANDSAT TM - art. no. 67863M

Analysis of the tropical forest vegetation change in Xishuan...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Yang, Cunjian He, Rong Luo, Shanxi He, Binbin Univ Elect Sci & Technol China Inst Geospatial Informat Sci & Technol Chengdu 610054 Peoples R China

ISBN: (纸本)9780819469502

The tropical forest change of Xishaungbanna in P.R. of China was studied here, which includes several steps. Firstly, The LANDSAT TM images acquired respectively on March 26, 1987, February 10, 1997, and March 6, 2000 were geometrically corrected, matched, and radiantly corrected. Secondly, the Normal Different Vegetation Index (NDVI) was formulated for the three periods. Thirdly, the false color composition images were achieved respectively for the three periods, which were used for visually interpreting forest and non-forest. Fourthly, the forest areas of the three periods were extracted semi-automatically from NDVI of the three periods by their suitable threshold. Fifthly, the forest areas of the three periods were changed into vector form. Sixthly, the extracted forest area of the three periods in vector form superposes their false color composition images for evaluating their accuracies. Finally, the map of forest change among the three periods was made, and the forest change area was calculated. The research shows: The decreased tropical forest area between 1987 and 1997 is 234558 hectares, accounted for 19 percent of the tropical forest area in 1987. The decreased tropical forest area between 1997 and 2000 is 41237 hectares, accounted for 4.2 percent of the tropical forest area in 1997.

关键词： tropical forest normal different vegetation index LANDSAT TM

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Speckle reduction for target extraction in synthetic aperture radar images using adaptive space separation based on independent component analysis - art. no. 67870D

Speckle reduction for target extraction in synthetic apertur...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： An, Xifeng Li, Yutong NW Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

ISBN: (纸本)9780819469519

Proposed is a novel approach based on independent component analysis (ICA) for speckle reduction and target extraction of SAR (synthetic aperture radar) images using adaptive space separation with weighted information entropy incorporated. First the basis and the independent components are respectively obtained by ICA technique, and Weighted Information Entropy of the image is computed, then based on the threshold computed from function T-WIE (Threshold VS. Weighted-Information-Entropy), independent components are adaptively separated and the basis are classified accordingly. Thus, the image space is separated into two subspaces -'clean' and 'noise'. Then, a proposed nonlinear operator 'ABO' is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.

关键词： target extraction synthetic aperture radar independent component analysis adaptive space separation weighted information entropy

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Road extraction based on scansnake for Beijing-1 image

Road extraction based on scansnake for Beijing-1 image

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5th International Symposium on multispectral image Processing and Pattern recognition

作者： Gong, Jianming Yang, Xiaomei Wang, Min Lu, Jian Zhang, Dandan Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Chinese Acad Sci State Key Lab Resources & Environm Informat Syst Beijing Peoples R China Nanjing Normal Univ Sch Geograph Sci Nanjing Peoples R China

ISBN: (纸本)9780819469502

Beijing-1 small satellite image of 4m high resolution not only makes it possible to extract the detailed information that is difficult to be obtained from low-resolution images, but also brings out new research subjects for automatic extraction and identification of thematic information. The reason for this are as follows:(1) the obvious increase of data requires higher spatial and temporal efficiency of image data retrieval, display, processing, etc.;(2) due to the highly detailed information of high resolution image, under the influence of the Bidirectional Reflectance Distribution Function (BRDF), different parts of the same object may have different grey levels;together with factors such as object shadow, mutual cover, and cloud cover, the phenomenon of same object, different spectrum of high resolution images becomes more prominent, and the different object, same spectrum still exists, which brings greater difficulties to the work of information extraction [1][2]. The road of high resolution image has the following features: (1) the width of the road varies slightly and slowly;(2) the direction of the road varies slowly;(3) the local mean grey level of the road varies slowly;(4) the road is relatively long. Due to the increase of the resolution, the noises such as bridges, cars and trees along the road and its edge also increase. The paper proposes a new road extraction algorithm namely Scansnake aimed at the features of Beijing-1 images. A large amount of experiments proved that Scansnake algorithm has the advantage of object tracking, and under a series of complex conditions such as the variation of the width of the road and the change of grey feature distribution, Scansnake method can extract the road information of the high resolution Beijing-1 image.

关键词： Beijing-1 small satellite road extraction Scansnake algorithm section plane analysis

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Research on remote sensing image segmentation based on ant colony algorithm- take the land cover classification of middle Qinling Mountains for example

Research on remote sensing image segmentation based on ant c...

引用

mippr 2009 - multispectral image acquisition and Processing: 6th International Symposium on multispectral image Processing and Pattern recognition

作者： Mei, Xin Wang, Qian Wang, Quanfang Lin, Wenfang School of Resource and Environmental Science Hubei University Wuhan 11 Xueyuan Road Wuchang Wuhan Hubei 430062 China

ISBN: (纸本)9780819478054

Remote sensing image based on the complexity of the background features, has a wealth of spatial information, how to extract huge amounts of data in the region of interest is a serious problem. image segmentation refers to certain provisions in accordance with the characteristics of the image into different regions, and it is the key of remote sensing image recognition and information extraction. Reasonably fast image segmentation algorithm is the base of image processing;traditional segmentation methods have a lot of the limitations. Traditional threshold segmentation method in essence is an ergodic process, the low efficiency impacts on its application. The ant colony algorithm is a populationbased evolutionary algorithm heuristic biomimetic, since proposed, it has been successfully applied to the TSP, job-shop scheduling problem, network routing problem, vehicle routing problem, as well as other cluster analysis. Ant colony optimization algorithm is a fast heuristic optimization algorithm, easily integrates with other methods, and it is robust. Improved ant colony algorithm can greatly enhance the speed of image segmentation, while reducing the noise on the image. The research background of this paper is land cover classification experiments according to the SPOT images of Qinling area. The image segmentation based on ant colony algorithm is carried out and compared with traditional methods. Experimental results show that improved the ant colony algorithm can quickly and accurately segment target, and it is an effective method of image segmentation, it also has laid a good foundation of image classification for the follow-up work. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Heuristic methods

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Color component prediction in multispectral space based on rotated principal component analysis

Color component prediction in multispectral space based on r...

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5th International Symposium on multispectral image Processing and Pattern recognition

作者： Xu, Faqiang Wan, Xiaoxia Zhu, Yuanhong Wuhan Univ Sch Remote Sensing Informat Engn 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Wuhan Univ Dept Printing & Packaging Wuhan 430079 Hubei Peoples R China Shenzhen Polytech Sch Media & Commun Shenzhen 518055 Guangdong Peoples R China

ISBN: (纸本)9780819469519

Aiming at the problem of negative index in the spectral color space built by means of traditional principal component analysis (PCA), a method of color component prediction based on rotated principal component analysis (RPCA) is proposed, which performs the rotating transformation from initial eigenvectors to a set of all-positive vectors as the physical basis color components while retaining the cumulative ratio of the variance contributions of significant principal components to the original multispectral space to the maximum extent. The rotated column vectors should be also polarized between 0 and 1. The spectral database of Munsell Matte Collection I is used for experiment. The experimental results show that the novel method of prediction not only uncovers the real color components of the target image better but reconstructs the normalized spectra data set with a high colorimetric and spectral accuracy. Thereinto, the colorimetric errors of the four estimated components reconstruction for more than 96 percent of the samples in Munsell Matte Collection I are less than 3 units of color difference acceptable.

关键词： color match color component prediction RPCA varimax all-positive vector polarization

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Proceedings of SPIE: mippr 2007: multispectral image Processing

Proceedings of SPIE: MIPPR 2007: Multispectral Image Process...

引用

mippr 2007: multispectral image Processing

ISBN: (纸本)9780819469519

The proceedings contain 96 papers. The topics discussed include: self-adaptive evolutionary algorithm for multispectral remote sensing image clustering;road detection in SAR images using a tensor voting algorithm;algorithm research on extracting terrain parameters using data of multi-return signal LiDAR;atmospheric correction algorithm for MODIS data of inland waters by using short-wave infrared bands;new feature extraction algorithm method based on gray multi-channel decomposition;fast processing of imaging spectrometer data cube based on FPGA design;simulation of atmospheric profile retrieval sensitivity with cloud from hyperspectral infrared data;design and implementation of the portable IR targets automatic detecting and tracking system based on USB2.0;and speckle reduction for target extraction in synthetic aperture radar images using adaptive space separation based on independent component analysis.

关键词： image processing

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Hyperspectal RS image classification based on kernel methods

Hyperspectal RS image classification based on kernel methods

引用

mippr 2007: Pattern recognition and Computer Vision

作者： Guopeng, Yang Hangye, Liu Xuchu, Yu Institute of Surveying and Mapping Information Engineering University Zhengzhou Henan 450052 China

ISBN: (纸本)9780819469526

Hyperspectral RS technology organically combines the radiation information and the space information. The spectrum information, which the hyperspectral image enriches, can be better to carry on the ground target classification, compare with panchromatic remote sensing image and multispectral remote sensing image. As support vector machine was applied to many fields successfully recent years, using kernel methods, the classic linear methods can cope with the nonlinear problem, which was called the 3rd revolution of pattern analysis algorithms. This paper introduced two classifying methods for hyperspectral image based on kernel function, Support Vector Machine and Kernel Fisher Discriminant analysis, and studied the selection of kernel function and its parameters as well as multi-class decomposition. We use radial basic function kernel, one against one or one against rest decomposition methods to construct multi-class classifier, and optimize parameter selection using cross-validating grid search to build an effective and robust kernel classifier. It is verified that, through the OMIS and AVIRIS image classifying experiments, comparing with common image classifying methods, kernel classifying method can avoid Hughes phenomenon, thus improve the classifying accuracy.

关键词： image classification

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