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Conference on International Conference on image processing and pattern recognition in Industrial Engineering

作者： Yu Rong Cai Bofeng Wen Qinke Chinese Acad Sci Inst Remote Sensing Applicat Beijing 100101 Peoples R China

ISBN: (纸本)9780819483294

Vegetation indices (VIs) are essential parameters widely used in the biosphere remote sensing retrieval, and the relationship between the same vegetation indexes derived from different sensors is critical to long-term monitoring of land surface properties. In this paper, MSAVI data derived from visible and near-infrared data acquired by the ASTER and SPOT4 sensors were compared over the same time periods and pixel size. The results showed that the two VIs play a higher correlation in high data field. ASTER MSAVI is more sensitive to vegetation coverage information. SPOT MSAVI overvalues the local vegetation reflection signals significantly. The linear relationship between vegetation coverage and MSAVI requires field sampling data to complete correction.

关键词： vegetation index ASTER SPOT4 MSAVI correlation analysis feature space

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GRAPH BASED recognition OF GRID pattern IN STREET NETWORKS

GRAPH BASED RECOGNITION OF GRID PATTERN IN STREET NETWORKS

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Joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science

作者： Tian, Jing Ai, Tinghua Jia, Xiaobin Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Wuhan Univ Minist Educ Key Lab Geog Informat Syst Wuhan 430079 Peoples R China

ISBN: (纸本)9783642259258

pattern recognition is an important step in map generalization. pattern recognition in street network is significant for street network generalization. A grid is characterized by a set of mostly parallel lines, which are crossed by a second set of parallel lines with roughly right angle. Inspired by object recognition in image processing, this paper presents an approach to the grid recognition in street network based on graph theory. Firstly, the bridges and isolated points of the network are identified and deleted repeatedly. Secondly, the similar orientation graph is created, in which the vertices represent street segments and the edges represent the similar orientation relation between streets. Thirdly, the candidates are extracted through graph operators such as finding connected component, finding maximal complete sub-graph, join and intersection. Finally, the candidate are evaluated by deleting bridges and isolated lines repeatedly, reorganizing them into stroke models, changing these stroke models into street intersection graphs in which vertices represent strokes and edges represent strokes intersecting each other, and then calculating the clustering coefficient of these graphs. Experimental result shows the proposed approach is valid in detecting the grid pattern in lower degradation situation.

关键词： Spatial information Sciences Cartography Generalization pattern Networks Vector

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An Investigation on Water Quality of Darlik Dam Drinking Water using Satellite images

THESCIENTIFICWORLDJOURNAL

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THESCIENTIFICWORLDJOURNAL 2010年第1期10卷 1293-1306页

作者： Alparslan, Erhan Coskun, H. Gonca Alganci, Ugur TUBITAK Marmara Res Ctr Earth & Marine Sci Inst TR-41470 Gebze Kocaeli Turkey Istanbul Tech Univ Fac Civil Engn Dept Remote Sensing TR-34469 Istanbul Turkey Istanbul Tech Univ Ctr Satellite Commun & Remote Sensing TR-34469 Istanbul Turkey

Darlik Dam supplies 15% of the water demand of Istanbul Metropolitan City of Turkey. Water quality (WQ) in the Darlik Dam was investigated from Landsat 5 TM satellite images of the years 2004, 2005, and 2006 in order to determine land use/land cover changes in the watershed of the dam that may deteriorate its WQ. The images were geometrically and atmospherically corrected for WQ analysis. Next, an investigation was made by multiple regression analysis between the unitless planetary reflectance values of the first four bands of the June 2005 Landsat TM image of the dam and WQ parameters, such as chlorophyll-a, total dissolved matter, turbidity, total phosphorous, and total nitrogen, measured at satellite image acquisition time at seven stations in the dam. Finally, WQ in the dam was studied from satellite images of the years 2004, 2005, and 2006 by pattern recognition techniques in order to determine possible water pollution in the dam. This study was compared to a previous study done by the authors in the Kucukcekmece water reservoir, also in Istanbul City.

关键词： water pollution remote sensing geographic information systems multiregression analysis pattern recognition watershed management

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Robust Pruning of Training patterns for Optimum-Path Forest Classification Applied to Satellite-Based Rainfall Occurrence Estimation

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IEEE GEOSCIENCE AND remote sensing LETTERS 2010年第2期7卷 396-400页

作者： Papa, Joao Paulo Falcao, Alexandre Xavier de Freitas, Greice Martins Heuminski de Avila, Ana Maria Univ Estadual Campinas Inst Comp BR-13083970 Campinas SP Brazil Univ Estadual Campinas Sch Elect & Comp Engn BR-13083970 Campinas SP Brazil Univ Estadual Campinas Ctr Meteorol & Climat Res Appl Agr BR-13083970 Campinas SP Brazil

The decision correctness in expert systems strongly depends on the accuracy of a pattern classifier, whose learning is performed from labeled training samples. Some systems, however, have to manage, store, and process a large amount of data, making also the computational efficiency of the classifier an important requirement. Examples are expert systems based on image analysis for medical diagnosis and weather forecasting. The learning time of any pattern classifier increases with the training set size, and this might be necessary to improve accuracy. However, the problem is more critical for some popular methods, such as artificial neural networks and support vector machines (SVM), than for a recently proposed approach, the optimum-path forest (OPF) classifier. In this letter, we go beyond by presenting a robust approach to reduce the training set size and still preserve good accuracy in OPF classification. We validate the method using some data sets and for rainfall occurrence estimation based on satellite image analysis. The experiments use SVM and OPF without pruning of training patterns as baselines.

关键词： Expert systems image analysis pattern recognition rainfall estimation remote sensing

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LOG-POLAR AND POLAR image FOR recognition TARGETS 30

LOG-POLAR AND POLAR IMAGE FOR RECOGNITION TARGETS

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IEEE International Geoscience and remote sensing Symposium

作者： Toumi, A. Khenchaf, A. ENSIETA E3I2 EA3876 F-29806 Brest 9 France

ISBN: (纸本)9781424495665

We describe in this paper, data processing algorithms applied on radar image in order to extract feature descriptors and then to perform recognition task. Several kinds of descriptors can be used to acquire information about target characteristics from radar images such as ISAR (Inverse Synthetic Aperture Radar) images. This paper presents two types of vector descriptors extracted via two minds of transformed images so-called polar and log-polar images obtained respectively from the polar and log-polar mapping. In order to guarantee the invariance of some geometrical transformation, additional processing are proposed. In this paper, we present the polar and log-polar transformations and then the classification scheme adapted on correspondent polar and log-polar templates. In the classification step, log-polar and polar mapping results are compared using adapted classification scheme.

关键词： Target recognition image processing Classification

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Parallel Algorithm Design for remote sensing image processing in the PC Cluster Environment

Parallel Algorithm Design for Remote Sensing Image Processin...

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18th International Conference on Geoinformatics

作者： Wang, Hongping Zhang, Juan Liu, Xiuguo Huang, Xiaodong China Univ Geosci Fac Informat Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9781424473021

In the PC cluster environment, parallel algorithms can significantly improve the efficiency of remote sensing image processing. The remote sensing dataset is the raster data stored by order of band, therefore, it is feasible to assign executable tasks to some computing nodes by band and complete the processing tasks together through communicating each other amount the computing nodes by MPI interface. In addition, the multi-thread processing algorithm is scheduled based on OpenMP library toward the single computing node with multi-core CPU. The experiment evaluates image radiation performance about the four-band HJ-1A CCD remote sensing image in the PC cluster environment which consists of 4 sets of dual-core computers. Its performance is increased by 6 times to 6.5 times comparing with a single PC. Through validating, the dual level design pattern is available based on integrating MPI and OpenMP to improve the efficiency of remote sensing image processing.

关键词： Parallel Algorithm remote sensing image processing MPI OpenMP PC Cluster Environment

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REAL-TIME image processing FOR ROAD TRAFFIC DATA EXTRACTION FROM AERIAL imageS

REAL-TIME IMAGE PROCESSING FOR ROAD TRAFFIC DATA EXTRACTION ...

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ISPRS Technical Commission VII Symposium - 100 Years ISPRS - Advancing remote sensing Science

作者： Rosenbaum, D. Leitloff, J. Kurz, F. Meynberg, O. Reize, T. DLR German Aerosp Ctr Remote Sensing Technol Inst D-82234 Wessling Germany

A world with growing individual traffic requires sufficient solutions for traffic monitoring and guidance. The actual ground based approaches for traffic data collection may be barely sufficient for everyday life, but they will fail in case of disasters and mass events. Therefore, a road traffic monitoring solution based on an airborne wide area camera system has been currently developed by DLR. Here, we present a new image processing chain for real-time traffic data extraction from high resolution aerial image sequences with automatic methods. This processing chain is applied in a computer network as part of an operational sensor system for traffic monitoring onboard a DLR aircraft. It is capable of processing aerial images obtained with a frame rate of up to 3 Hz. The footprint area of the three viewing directions of an image exposure with three cameras is 4 x 1 km at a resolution of 20 cm (recorded at a flight height of 1500 m). The processing chain consists of a module for data readout from the cameras and for the synchronization of the images with the GPS/IMU navigation data (used for direct georeferencing) and a module for orthorectification of the images. Traffic data is extracted by a further module based on a priori knowledge from a road database of the approximate location of road axes in the georeferenced and orthorectified images. Vehicle detection is performed by a combination of Adaboost using Haar-like features for pixel wise classification and subsequent clustering by Support Vector Machine based on a set of statistical features of the classified pixel. In order to obtain velocities, vehicle tracking is applied to consecutive images after performing vehicle detection on the first image of the burst. This is done by template matching along a search space aligned to road axes based on normalized cross correlation in RGB color space. With this processing chain we are able to obtain accurate traffic data with completeness and correctness both higher than

关键词： Monitoring recognition Orthorectification Georeferencing image pattern Sequences Tracking

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TRANSFORMATION-INVARIANT EXTRACTION OF MULTI-LOCATION image FEATURES FROM remote sensing imageRY 30

TRANSFORMATION-INVARIANT EXTRACTION OF MULTI-LOCATION IMAGE ...

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30th IEEE International Geoscience and remote sensing Symposium (IGARSS) on remote sensing - Global Vision for Local Action

作者： Palenichka, Roman M. Lakhssassi, Ahmed Zaremba, Marek B. Univ Quebec Outaouais Dept Comp Sci & Engn Quebec City PQ Canada

ISBN: (纸本)9781424495665

A novel method of transformation-invariant feature extraction called multi-location saliency pattern is proposed in this paper for object recognition and image matching. Multi-location image features are extracted in salient image points, which indicate image locations with high intensity contrast, region homogeneity and shape saliency. Three distinctive types of fragment descriptors are extracted to form the descriptor vector: pose, regional shape, and intensity (texture) descriptors. Pose characteristics and regional shape descriptors are made invariant to image similarity transformations.

关键词： feature extraction salient point image matching attention operator regional shape

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An Automatic Registration Method for Different Temporal remote sensing images Based on Improved Fourier-Mellin Algorithm

An Automatic Registration Method for Different Temporal Remo...

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International Conference on Space Information Technology 2009

作者： Li, Rui Sun, Jiulin Yin, Fang Guo, Fusheng Wang, Wantong Henan Univ Coll Environm & Planning Kaifeng 475000 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100080 Peoples R China

ISBN: (纸本)9780819480798

Multi-temporal remote sensing image registration is the key step of change detection, and because of the remarkable difference and the probably unknown of sensor parameters, the automatic registration of different temporal remote sensing images is very difficult. image registration based on Fourier-Mellin transform( FMT) is a global and phase correlation method, which is based on Fourier and Log-polar transform. This method finds the transformation parameters for registration of the images while working in the frequency-domain and it is resilient to noise, occlusions and so on. In this paper, an improved approach based on Fourier-Mellin algorithm is proposed for the registration. Spectrum aliasing and resampling interpolation will bring errors during Fourier-Mellin transform. To get a better registration result, we have improved it by adding window function and filtering to reduce spectrum aliasing and increase the robustness.

关键词： image registration remote sensing Fourier transforms Sensors Optical filters image processing Linear filtering Defense and security Geographic information systems pattern recognition

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Multi-class target recognition based on adaptive feature selection

Multi-class target recognition based on adaptive feature sel...

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Conference on Automatic Target recognition XX - Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV - and Optical pattern recognition XXI

作者： Wang, Yuehuan Yao, Wei Song, Yunfeng Sang, Nong Zhang, Tianxu Huazhong Univ Sci & Technol Inst Pattern Recognit & AI Wuhan 430074 Peoples R China Natl Key Lab Sci & Tech Multi Spectral Informat Proc Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780819481603

In this paper, a new approach of multi-class target recognition is proposed for remote sensing image analysis. A multi-class feature model is built, which is based on sharing features among classes. In order to make the recognition process efficient, we adopted the idea of adaptive feature selection. In each layer of the integrated feature model, the most salient and stable feature are selected first, and then the less ones. Experiments demonstrated the approach proposed is efficient in computation and is adaptive to scene variation.

关键词： remote sensing image adaptive feature selection multiclass target recognition

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