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

共 708 条记录，以下是241-250 订阅

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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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Segmentation of Noisy images Using Improved Distance Regularized Level Set Evolution

Segmentation of Noisy Images Using Improved Distance Regular...

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IEEE International conference on Circuit ,Power and Computing Technologies (ICCPCT)

作者： Yugander, P. Reddy, G. Raghotham KITS Warangal Dept ECE Ts India

ISBN: (纸本)9781509049677

In this paper, we propose a novel framework for the segmentation of noisy images by incorporating the advantages of k-means clustering and distance regularized level set evolution (DRLSE). Level set methods and active contour models (ACMs) plays a vital role in the applications of image processing, robot vision, object recognition and computer vision. DRLSE model has recently become a powerful technique for image segmentation. DRLSE model eliminates the reinitialization problem in conventional level set method. DRLSE has been applied successfully into some fields like medical imaging, remote sensing and computer vision. However when it is applied to noisy images, It leads to significant drawbacks (number of iterations and computational time is increased). In order to avoid disadvantages of conventional DRLSE, we introduce a method to combine the median filtering, k-means clustering and DRLSE model. Firstly a noise free image is extracted by median filtering;then k-means clustering is applied to denoised image. The last stage is that the DRLSE model is applied for the extraction of object boundaries with pre segmentation process. The accuracy and the efficiency of the algorithm can be described on various noisy magnetic resonance (MR) brain images. The experiments show that our proposed method is more effective for noisy images.

关键词： image segmentation Median filter k-means (KM) clustering DRLSE

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An Unsupervised Change Detection Approach for remote sensing image Using SURF and SVM

An Unsupervised Change Detection Approach for Remote Sensing...

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7th Chinese conference on pattern recognition (CCPR)

作者： Wu, Lin Wang, Yunhong Long, Jiangtao Chongqing Commun Coll Chongqing 400035 Peoples R China Beihang Univ Beijing 100191 Peoples R China

ISBN: (纸本)9789811030055;9789811030048

In this paper, we propose a novel approach for unsupervised change detection by integrating Speeded Up Robust Features (SURF) key points and Support Vector Machine (SVM) classifier. The approach starts by extracting SURF key points from both images and matches them using RANdom SAmple Consensus (RANSAC) algorithm. The matched key points are then viewed as training samples for unchanged class;on the other hand, those for changed class are selected from the remaining SURF key points based on Gaussian mixture model (GMM). Subsequently, training samples are utilized for training a SVM classifier. Finally, the classifier is used to segment the difference image into changed and unchanged classes. To demonstrate the effect of our approach, we compare it with the other four state-of-the-art change detection methods over three datasets, meanwhile extensive quantitative and qualitative analysis of the change detection results confirms the effectiveness of the proposed approach, showing its capability to consistently produce promising results on all the datasets without any priori assumptions.

关键词： Change detection remote sensing image Speeded Up Robust Features (SURF) Support Vector Machine (SVM)

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Multisource Classification and pattern recognition methods for Polar Geospatial Information Mining using WorldView-2 data 6

Multisource Classification and Pattern recognition methods f...

引用

conference on Multispectral, Hyperspectral, and Ultraspectral remote sensing Technology, Techniques and Applications VI

作者： Khopkar, Parag S. Jawak, Shridhar D. Luis, Alvarinho J. Univ Pune Dept Geog Pune Maharashtra India ISRO Ctr Space Applicat Ahmadabad Gujarat India Govt India Minist Earth Sci Natl Ctr Antarctic & Ocean Res ESSO Vasco Da Gama India

ISBN: (纸本)9781510601215

Current research study emphasizes the importance of advanced digital image processing methods in order to delineate between various LULC features. In the case of the Antarctica, the present LC (snow/ice, landmass, water, vegetation etc.) and the present LU (research stations of various nations) needs to be mapped accurately for the hassle free routine activities. Geo-location has become the most important part of geosciences studies. In this paper we have tried to locate three most important features (snow/ice, landmass, and water) and also have extracted the extent of the same using the multisource classification (image fusion/pansharpening) and pattern recognition (supervised/unsupervised methods, index ratio methods). Innovation in developing spectral index ratios has led us to come up with an unique ratio named Normalized Difference Landmass Index (NDLI) which performed better (Avg. Bias: 51.99m) than other ratios such as Normalized Difference Snow/Ice Index (NDSii) (Avg. Bias: -1572.11m) and Normalized Difference Water Index (NDWI) (Avg. Bias: 1886.60m). The practiced trial and error methodology quantifies the productivity of not only the classification methods over one other but also that of the fusion methods. In present study, classifiers used (Mahalanobis and Winner Takes All) performed better (Avg. Bias: 122.16 m) than spectral index ratios (Avg. Bias: 620.16 m). The study also revealed that newly introduced bands in WorldView-2, band 1 (Coastal Blue), 4 (Yellow), 6 (Red-edge) and 8 (Near Infrared-2) along with traditional bands have the capacity to mine the polar geospatial information with utmost accuracy and efficiency.

关键词： NDLI Pansharpening WorldView-2 pattern recognition

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Multi-level L2- L1-structured regularization technique for recovery of material abundances maps from hyperspectral remote sensing imagery

Multi-level L2- L1-structured regularization technique for r...

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2016 International conference on image processing, Computer Vision, and pattern recognition, IPCV 2016

作者： Shkvarko, Yuriy Perez, Pedro López, Josué García, Guillermo Santos, Stewart CINVESTAV – IPN Guadalajara Jalisco Mexico Electronics Department University of Guadalajara Guadalajara Jalisco Mexico

ISBN: (纸本)1601324421

- This paper addresses a novel approach to the problem of feature enhanced recovery of material abundances maps from hyperspectral remote sensing imagery. In contrast to the competing methods that exploit separately vertex component analysis and minimum volume constrained nonnegative matrix factorization requiring knowledge of endmembers libraries, the proposed approach suggests a multilevel l2-l1-structured robust spatial regularization (MLl2l1SR) based technique for abundances maps recovery that avoids the use of endmembers libraries. Traditionally, a pure pixel search is performed to initialize the endmembers matrix estimation followed by nonnegative matrix factorization. In contrast, in the proposed approach, the spectral signatures are recovered from the original data in an adaptive fashion via a nonlinear estimation procedure with l2-l1-structured robust spatial regularization performed in a multi-step iterative fashion that does not require knowledge of endmember libraries, nor pure pixels. Computer simulations on synthetic images corroborate the efficiency of the new multistage MLl2l1SR method that is robust against model uncertainties and outperforms the competing techniques in the accuracy of recovery of abundances maps. © CSREA Press.

关键词： Recovery

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Road Extraction Based on Direction Consistency Segmentation 7th

Road Extraction Based on Direction Consistency Segmentation

引用

7th Chinese conference on pattern recognition (CCPR)

作者： Ding, Lei Yang, Qimiao Lu, Jun Xu, Junfeng Yu, Jintao Zhengzhou Inst Surveying & Mapping Dept Photogrammetry & Remote Sensing Zhengzhou 450001 Henan Peoples R China 61206 Troops Dalian 116000 Peoples R China

ISBN: (纸本)9789811030024;9789811030017

A common strategy for road extraction from remote sensing images is classification based on spectral information. However, due to a common phenomenon that different objects can be with similar spectral characteristics, classification results usually contain many interference regions which do not correspond to any road entity. To solve this problem, a road extraction method based on direction consistency segmentation is proposed in this paper. In binary road classification images, considering that road regions in these images usually have consistent local directions, pixels with similar main directions are merged into objects. After acquiring these objects, geometric measurements such as LFI (Linear Feature Index) and region area are calculated and a segment-linking algorithm is used to recognize and extract road objects among them. Various test images are used to verify the effectiveness of this method and contrast experiments are performed between the proposed binary image processing method and two existing methods. Experimental results show that this method has advantages in both accuracy, computational efficiency and stability, which can be used to extract road regions in remote sensing images at different resolutions.

关键词： Road extraction Direction consistency segmentation Geometric feature Segment-linking

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A star identification algorithm for large FOV observations 22

A star identification algorithm for large FOV observations

引用

conference on image and Signal processing for remote sensing XXii

作者： Duan, Yu Niu, Zhaodong Chen, Zengping Natl Univ Def Technol Sci & Technol Automat Target Recognit Lab 109 Deya Rd Changsha 410073 Hunan Peoples R China

ISBN: (数字)9781510604131

ISBN: (纸本)9781510604124;9781510604131

Due to the broader extent of observation and higher detection probability of space targets, large FOV (field of vision) optical instruments are widely used in astronomical applications.. However, the high density of observed stars and the distortion of the optical system often bring about inaccuracy in star locations. So in large FOV observations, many conventional star identification algorithms do not show very good performance. In this paper, we propose a star identification method with a low requirement for observation accuracy and thus suitable for large FOV circumstances. The proposed method includes two stages. The former is based on the match group algorithm, in addition to which we exploit the information of differential angles of inclination for verification. The inclinations of satellite stars are computed by reference to the selected pole stars. Then we obtain a set of identified stars for further recognition. The latter stage involves four steps. First, we derive the relationship between the rectangular coordinates of catalog stars and sensor stars with the identified locations obtained. Second, we transform the sensor coordinates to the catalog coordinates and find the catalog stars at close range as candidates. Third, we calculate the angle of inclination of each unidentified sensor star in relation to the nearest previously identified one, and the angular separation between them as well, to compare with those of the candidates. At last, candidates satisfying the limitations are considered the appropriate correspondences. The experimental results show that in large FOV observations, the proposed method presents better performance in comparison with several typical star identification methods in open literature.

关键词： Star identification pattern recognition large FOV match

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Hierarchical image Matching Method Based on Free-Form Linear Features

Hierarchical Image Matching Method Based on Free-Form Linear...

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7th Chinese conference on pattern recognition (CCPR)

作者： Chen, Xiaowei Guo, Haitao Zhao, Chuan Zhang, Baoming Lin, Yuzhun Zhengzhou Inst Surveying & Mapping Zhengzhou Henan Peoples R China

ISBN: (纸本)9789811030055;9789811030048

In order to better resolve the conflict between the full use and effective description of the Linear Feature information in the study of free form linear feature (FFLF) matching, this paper proposed a remote sensing image matching method using the hierarchical matching strategy. First the edge features of the image were detected and tracked, in order to extract the free-form sub-pixel linear features with better continuity;in the coarse matching process, the closed linear features (CLF), linear feature intersection (LFI) and corner (LFC) were selected as conjugated entity, and then the false match was gradually eliminated based on the area, angle and other geometry information as well as the parameter distribution features of the model determined by the conjugate features combination to be selected, finally the initial value of accurate matching was determined by the conjugate features;in the accurate matching process, based on multi-level two-dimensional iterative closest point (ICP) algorithm, sub-pixel edge points were orderly used with the sampling rate from low to high for matching. Experimental results show that this method has stable performance for the coarse matching;high accuracy and precision of the coarse matching can provide the initial matching parameters of high precision for accurate matching;accurate matching can reach the sub-pixel level precision equal to the least square matching and can better achieve stable and accurate matching for the images with smaller affine deformation.

关键词： Free-form linear feature (FFLF) image matching Hierarchical matching strategy Closed linear feature (CLF) Linear feature corner (LFC) Linear feature intersection (LFI) Iterative closest point (ICP) algorithm

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Detailed remote sensing of High Resolution Planetary images by Artificial Neural Network 25th

Detailed Remote Sensing of High Resolution Planetary Images ...

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25th International conference on Artificial Neural Networks (ICANN)

作者： Foroutan, Marzieh Univ Calgary Calgary AB Canada

ISBN: (纸本)9783319447810;9783319447803

Increasing the application of high resolution spatial data such as high resolution satellite or Unmanned Aerial Vehicle (UAV) images from Earth as well as High Resolution Imaging Science Experiment (HiRISE) images from Mars makes it necessary to develop automated techniques capable of extracting their detailed information. Furthermore, model validation based on multi-temporal images in different Environmental management issues and geophysical problems such as climate change effects demand more precise imagery-processing in remote sensing discipline. This study intends to develop a methodology based on Artificial Neural Network (ANN) algorithm to achieve the automatic extraction of small footprints from these high resolution images in order to facilitate and speed up image analysis along with the improvement of the results accuracy. Mapping different types of micro-landforms, such as aeolian, glacier and volcanic minor features, and extracting their morphometric and pattern information on planets is challenging because of their small footprint on satellite images and their large numbers or high density in small areas. Unfortunately available feature extraction modules of remote sensing software don’t work properly for the size of these features. Previous studies traced and digitized small features manually (e.g. [1]). This proposal intends to create a framework based on an unsupervised ANN algorithm to automatize outlining these features and extracting their metrics. ANN algorithm characteristics such as learning ability, abstraction with topology preservation, and visualization can be applied to complex tasks in different disciplines (e.g. [2, 3]). In this study HiRISE images from Mars as well as UAV images from Earth are used as preliminary data and other layers will be extracted from selective filters. Different settings have been examined for the best which captures small linear feature outlines most accurately from these high quality satellite images. We intend

关键词： remote sensing

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HD maps: Fine-grained road segmentation by parsing ground and aerial images

HD maps: Fine-grained road segmentation by parsing ground an...

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2016 IEEE conference on Computer Vision and pattern recognition, CVPR 2016

作者： Máttyus, Gellért Wang, Shenlong Fidler, Sanja Urtasun, Raquel Remote Sensing Technology Institute German Aerospace Center Germany Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes. Towards this goal, we propose an efficient approach that is able to estimate these fine grained categories by doing joint inference over both, monocular aerial imagery, as well as ground images taken from a stereo camera pair mounted on top of a car. Important to this is reasoning about the alignment between the two types of imagery, as even when the measurements are taken with sophisticated GPS+IMU systems, this alignment is not sufficiently accurate. We demonstrate the effectiveness of our approach on a new dataset which enhances KITTI [8] with aerial images taken with a camera mounted on an airplane and flying around the city of Karlsruhe, Germany.

关键词： Stereo image processing

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