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

共 707 条记录，以下是231-240 订阅

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pattern recognition algorithm using descriptors combined radio and visible spectra

Pattern recognition algorithm using descriptors combined rad...

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conference on Mobile Multimedia/image processing, Security, and Applications

作者： Richter, A. A. Kazaryan, M. L. Shakhramanyan, M. A. Voronin, V. V. Researching Inst AEROCOSMOS Moscow Russia Financial Univ Govt Russian Federat Vladikavkaz Branch Vladikavkaz Russia Don State Tech Univ Dept Radio Elect Syst Rostov Na Donu Russia

ISBN: (数字)9781510609440

ISBN: (纸本)9781510609433;9781510609440

This study presents a remote sensing application of using time series Landsat satellite images for monitoring the solid waste disposal site (WDS). We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For disposal site detection a variety steps of image processing used (calculation image average level of the earth's surface;filtering thresholds spectral brightness coefficients, the size of the connected components, the nature of reducing the level of height with the distance of the maximum level). The spatial geometric features of waste disposal facilities are analytically expressed by linear and radial characteristics from other objects of the earth surface. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

关键词： radio image cross-spectral imaging satellite images feature point descriptor

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Subsurface Classification of Objects Under Turbid Waters by Means of Regularization Techniques Applied to Real Hyperspectral Data 23

Subsurface Classification of Objects Under Turbid Waters by ...

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23rd SPIE conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery

作者： Carpena, Emmanuel Jimenez, Luis O. Arzuaga, Emmanuel Fonseca, Sujeily Reyes, Ernesto Figueroa, Juan Univ Puerto Rico Lab Appl Remote Sensing & Image Proc POB 9048 Mayaguez PR 00681 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

Improved benthic habitat mapping is needed to monitor coral reefs around the world and to assist coastal zones management programs. A fundamental challenge to remotely sensed mapping of coastal shallow waters is due to the significant disparity in the optical properties of the water column caused by the interaction between the coast and the sea. The objects to be classified have weak signals that interact with turbid waters that include sediments. In real scenarios, the absorption and backscattering coefficients are unknown with different sources of variability (river discharges and coastal interactions). Under normal circumstances, another unknown variable is the depth of shallow waters. This paper presents the development of algorithms for retrieving information and its application to the classification and mapping of objects under coastal shallow waters with different unknown concentrations of sediments. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. The retrieval of information requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and classification of hyperspectral data. The algorithms developed were applied to one set of real hyperspectral imagery taken in a tank filled with water and TiO2 that emulates turbid coastal shallow waters. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo of the water tank using a priori information in the form of stored spectral signatures, previously measured, of objects of interest.

关键词： remote sensing pattern recognition Subsurface sensing Inverse Models Regularization Shallow Waters Ocean Floor Mapping Benthic Habitats Mapping

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Vessel and oil spill early detection using COSMO satellite imagery

Vessel and oil spill early detection using COSMO satellite i...

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conference on remote sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions

作者： Revollo, Natalia V. Delrieux, Claudio A. UNS CONICET Dept Ingn Elect & Comp Av Alem 1253 Bahia Blanca Buenos Aires Argentina UNS CONICET Inst Invest Ingn Elect Avda Alem 1253 Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9781510613096;9781510613089

Oil spillage is one of the most common sources of environmental damage in places where coastal wild life is found in natural reservoirs. This is especially the case in the Patagonian coast, with a littoral more than 5000 km long and a surface above a million and half square km. In addition, furtive fishery activities in Argentine waters are depleting the food supplies of several species, altering the ecological equilibrium. For this reason, early oil spills and vessel detection is an imperative surveillance task for environmental and governmental authorities. However, given the huge geographical extension, human assisted monitoring is unfeasible, and therefore real time remote sensing technologies are the only operative and economically feasible solution. In this work we describe the theoretical foundations and implementation details of a system specifically designed to take advantage of the SAR imagery delivered by two satellite constellations (the SAOCOM mission, developed by the Argentine Space Agency, and the COSMO mission, developed by the Italian Space Agency), to provide real-time detection of vessels and oil spills. The core of the system is based on pattern recognition over a statistical characterization of the texture patterns arising in the positive and negative conditions (i.e., vessel, oil, or plain sea surfaces). Training patterns were collected from a large number of previously reported contacts tagged by experts in the National Commission on Space Activities (CONAE). The resulting system performs well above the sensitivity and specificity of other avalilable systems.

关键词： Pixel prototypes Vessel detection Oil spill detection SAR imagery image processing Statistical inference

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remote Biometric Verification for eLearning Applications: Where We Are 19th

Remote Biometric Verification for eLearning Applications: Wh...

引用

19th International conference on image Analysis and processing (ICIAP)

作者： Sanna, Pietro S. Marcialis, Gian Luca Univ Cagliari Dept Elect & Elect Engn Cagliari Italy

ISBN: (纸本)9783319685489;9783319685472

In recent years there has been a huge spread of online learning methods - eLearning - that have allowed learners of any type to enjoy well-structured lessons and articulated training course, with solid teaching methodologies. Many solutions and platforms have extremely useful and powerful features for the student;the innovative methodologies have brought unexpected results to all levels, multiple contexts, and areas of interest. In the e-learning there is an ongoing revolution that determines a strong impact on the training system on different fields: methodological, educational, social and technological. However, the identity of an individual - in eLearning - has always been the basis of some controversies. In this regard, a critical aspect concerns the User Identity verification and recognition. Currently there is an element of attention, the problem of examination is still to be done in the presence of the teacher in order to be permanently accredited. Presently are already used various user authentication and identification techniques, such as conventional User ID and password, challenging questions, and some biometric techniques. From this point of view, biometric technologies that have the feature of verifying their personal identity without being "stolen" or "lend" can provide a possible solution. Despite this fact, a few approaches have been proposed for eLearning applications. The purpose of this paper is to provide a summary of the state of the art of biometric methods applied to current eLearning solutions.

关键词： eLearning Biometrics pattern classification Authentication User identification Learning Management System MOOC Gamification Keystroke dynamics Stylometry

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Candidate Oil Spill Detection in SLAR Data A Recurrent Neural Network-based Approach 6

Candidate Oil Spill Detection in SLAR Data <i>A Recurrent Ne...

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6th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Oprea, Sergiu-Ovidiu Gil, Pablo Mira, Damian Alacid, Beatriz Univ Alicante Univ Inst Comp Res San Vicente Del Raspeig Spain Univ Alicante Dept Phys Syst Engn & Signal Theory San Vicente Del Raspeig Spain

ISBN: (纸本)9789897582226

Intentional oil pollution damages marine ecosystems. Therefore, society and governments require maritime surveillance for early oil spill detection. The fast response in the detection process helps to identify the offenders in the vast majority of cases. Nowadays, it is a human operator whom is trained for carrying out oil spill detection. Operators usually use image processing techniques and data analysis from optical, thermal or radar acquired from aerial vehicles or spatial satellites. The current trend is to automate the oil spill detection process so that this can filter candidate oil spill from an aircraft as a decision support system for human operators. In this work, a robust and automated system for candidate oil spill based on Recurrent Neural Network (RNN) is presented. The aim is to provide a faster identification of candidate oil spills from SLAR scanned sequences. So far, the majority of the research works about oil spill detection are focused on the classification between real oil spills and look-alikes, and they use SAR or optical images but not SLAR data. Moreover, the overall decision is usually taken by an operator, mainly due to the wide variety of types of look-alikes which cause false positives in the detection process using traditional NN. This work provides a RRN-based approach for candidate oil spill detection using SLAR data in contrast with the traditional Multilayer Perceptron Neural Network (MLP). The system is tested with time series data acquired from a SLAR sensor mounted on an aircraft. It achieves a success rate in detecting of 97%.

关键词： Oil Spill Detection Maritime Surveillance SLAR remote sensing RNN LSTM Classification

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SAR image time series analysis in vegetation classification using global and local information based linear discriminant analysis

SAR image time series analysis in vegetation classification ...

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IEEE Signal processing and Communications Applications (SIU)

作者： Ufuk Sakarya Caner Demirpolat TÜBITAK UZAY ODTÜ Ankara

ISBN: (纸本)9781509064953

Vegetation classification using SAR images is one of the research topics in remote sensing. In this paper, SAR image time series analysis in vegetation classification using global and local information based linear discriminant analysis is presented. It is experimentally demonstrated that the use of local pattern information in addition to global pattern information has increased both accuracy and time performance in vegetation classification using time-series TerraSAR-X images.

关键词： Synthetic aperture radar remote sensing Principal component analysis Linear discriminant analysis Time series analysis Vegetation mapping pattern recognition

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A Novel Vehicle Tracking Algorithm Based on Mean Shift and Active Contour Model in Complex Environment 2

A Novel Vehicle Tracking Algorithm Based on Mean Shift and A...

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conference on Automated Visual Inspection and Machine Vision ii

作者： Cai, Lei Wang, Lin Li, Bo Zhang, Libao Lv, Wen Minist Commun ITSC Res Inst Highway Key Lab Intelligent Transportat Syst Technol Beijing 100088 Peoples R China Beijing Normal Univ Coll Informat Sci & Technol 19 Xinjiekouwai Rd Beijing 100875 Peoples R China

ISBN: (纸本)9781510611146;9781510611139

Vehicle tracking technology is currently one of the most active research topics in machine vision. It is an important part of intelligent transportation system. However, in theory and technology, it still faces many challenges including real-time and robustness. In video surveillance, the targets need to be detected in real-time and to be calculated accurate position for judging the motives. The contents of video sequence images and the target motion are complex, so the objects can't be expressed by a unified mathematical model. Object-tracking is defined as locating the interest moving target in each frame of a piece of video. The current tracking technology can achieve reliable results in simple environment over the target with easy identified characteristics. However, in more complex environment, it is easy to lose the target because of the mismatch between the target appearance and its dynamic model. Moreover, the target usually has a complex shape, but the tradition target tracking algorithm usually represents the tracking results by simple geometric such as rectangle or circle, so it cannot provide accurate information for the subsequent upper application. This paper combines a traditional object-tracking technology, Mean-Shift algorithm, with a kind of image segmentation algorithm, Active-Contour model, to get the outlines of objects while the tracking process and automatically handle topology changes. Meanwhile, the outline information is used to aid tracking algorithm to improve it.

关键词： Machine vision image processing target recognition object tracking mean shift algorithm active contour model

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Multi-temporal and Multi-view Based remote sensing image Registration for Ground Surface Objects Change Monitoring

Multi-temporal and Multi-view Based Remote Sensing Image Reg...

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IEEE International conference on Electronic Measurement & Instruments

作者： Gao Xueyan Liu Xinang Yang Yang Hong Liang Laboratory of Pattern Recognition and Artificial Intelligence Yunnan Normal University Kunming China The Asset Management Department The First Affiliated Hospital of Kunming Medical University Kunming China School of Tourism and Geographical Sciences Yunnan Normal University Kunming China

remote sensing image registration is a key technology in the field of remote sensing image processing. However, the change of time and the change of view angle will cause the non-rigid distortion and low degree of overlap between the images, and further increase the difficulty of image registration. In order to solve the above problems, a registration algorithm of remote sensing image based on image geometric structural and gray information is presented. By comparing the multi-temporal and multi-angle remote sensing images with four similar algorithms (SIFT, CPD, RSOC, GLMDTPS), our algorithm is superior to other algorithms in the comparison experiment, and shows the optimal registration performance.

关键词： remote sensing image multi-temporal multi-view geomtric characteristics structure non-rigid distortion

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Linear unmixing for subsurface estimation on coastal shallow waters

Linear unmixing for subsurface estimation on coastal shallow...

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8th International conference of pattern recognition Systems (ICPRS 2017)

作者： Emmanuel Carpena-Colón Luis O. Jiménez-Rodríguez Emmanuel Arzuaga-Cruz Laboratory for Applied Remote Sensing and Image Processing (LARSIP) Electrical and Computer Engineering Department University of Puerto Rico - Mayaguez Campus

ISBN: (纸本)9781785616525

This paper presents the development and enhancement of a subsurface (underwater) linear unmixing algorithm, called LIGU, specially conceived to determine individual contributions to the measured signal of given spectral reflectance of objects at the bottom of coastal shallow waters. This algorithm is part of a Hyperspectral Coastal image Analysis Toolbox (HyCIAT), which is a repository of tools to be used to retrieve information from object embedded in a diffusive and murky medium. This paper discusses mathematical formulations behind the subsurface unmixing algorithm LIGU and presents enhancements made to the algorithm. Finally, quantitative and qualitative results will be presented using a hyperspectral data set from a controlled and well known environment. These results provide noticeable quantitative improvement when compared with the same algorithms without the enhancements.

关键词： Linear Unmixing Hyperspectral Data Subsurface sensing Inverse Methods Estimation

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Fast Extraction of Dominant Planes in MLS-data of Urban Areas 9

Fast Extraction of Dominant Planes in MLS-data of Urban Area...

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9th IAPR Workshop on pattern recognition in remote sensing (PRRS)

作者： Gordon, Marvin Fraunhofer Inst Optron Syst Technol & Image Explo Ettlingen Germany

ISBN: (纸本)9781509050413

Many current LIDAR systems generate huge amounts of 3D points, therefore efficient and fast data processing is essential. In urban areas surfaces are often planar and plane patches are an efficient representation for localization purposes. Our approach thus stores the points in a voxel grid and afterwards extracts the dominant plane using RANSAC-based plane fitting. Classical RANSAC-based plane extraction can result in improper planes, especially in the case of steps like curbsides. Different solutions have been presented, but they are computational demanding. We tackle this problem by extending the loss function of the standard RANSAC. This solves the problem in most cases and has nearly no impact on the runtime. We show the improvement using data of an urban environment, which were recorded by an MLS-system.

关键词： RANSAC plane estimation MLS LIDAR

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