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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291-300 订阅

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URBAN ROAD EXTRACTION VIA GRAPH CUTS BASED PROBABILITY PROPAGATION

URBAN ROAD EXTRACTION VIA GRAPH CUTS BASED PROBABILITY PROPA...

引用

IEEE International conference on image processing

作者： Guangliang Cheng Ying Wang Yongchao Gong Feiyun Zhu Chunhong Pan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science

ISBN: (纸本)9781479957521

In this paper, we propose a graph cuts (GC) based probability propagation approach to automatically extract road network from complex remote sensing images. First, the support vector machine (SVM) classifier with a sigmoid model is applied to assign each pixel a posterior probability of being labelled as road class, which avoids the weaknesses of hard labels in general SVM. Then a GC based probability propagation algorithm is employed to keep the extracted road results smooth and coherent, which can reduce the connections between roads and road-like objects. Finally, a road-geometrical prior is considered to refine the extraction result, so that the non-road objects in images can be removed. Experimental results on two remote sensing image datasets indicate the validity and effectiveness of our method by comparing with two other approaches.

关键词： Road extraction Support Vector Machine Sigmoid model Graph cuts Probability propagation

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pattern recognition: Advanced development, techniques and application for image retrieval

Pattern recognition: Advanced development, techniques and ap...

引用

International conference on Communication and Network Technologies (ICCNT)

作者： A.A. Khodaskar S. A. Ladhake Computer Science and Engineering SIPNA COET Amravati India

Objective of our paper is to discuss latest pattern recognition applications, techniques and development. pattern recognition has been demanding field from many years. We are also discuss driving force behind its swift development, that is pattern recognition is used to give human recognition intelligence to machine which is soul of today's many modern application. It acts as wheel of many techniques and applications in different fields. pattern recognition is recognition process which recognizes a pattern using a machine or computer. It is a study of ideas and algorithms that provide computers with a perceptual capability to put abstract objects, or patterns into categories in a simple and reliable way. The development and demand of pattern recognition technology is very fast and applications of pattern recognition are increase day by day. To fulfill this need, more and more researchers and scientists are evolved new pattern recognition techniques and apply them to many real life applications such as agriculture, robotics, biometrics, medical diagnosis, life form analysis, image processing, process control, information management systems, aerial photo interpretation, weather prediction, sensing of life on remote planets, behavior analysis, , Speech recognition, automatic diseases detection system in the infected plants, cancer detection system etc. with combination of other technology. Particular, in image retrieval system, pattern recognition play important for improving accuracy of image retrieval by using variety of recent techniques and their combination.

关键词： Feature extraction Transforms Data mining Support vector machine classification image retrieval Diseases

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Possibility theory for supervised classification of remotely sensed images: A study case in an urban area in Algeria

Possibility theory for supervised classification of remotely...

引用

International conference of Soft Computing and pattern recognition (SOCPAR)

作者： Radja Kheddam Aichouche Belhadj-Aissa Image processing and radiation laboratory University of science and technology (USTHB) Algiers Algeria

In this paper we present a possibilistic classifier of multispectral remotely sensed images. This classifier developed in the framework of possibility theory is based on a fusion process using several kinds of combination operators (conjunctive and disjunctive). Unlike the probabilistic classifier which can model only the data uncertainty through a probability measure, the possibilistic classifier has the ability to handle both uncertainty and imprecision of pixel classification through a possibility and a necessity measures. These two classifiers are applied to classify a multispectral image acquired on 2001 by ETM+ sensor of Landsat-7 satellite. This multi-band image covers a north-eastern part of Algiers (Algeria). Compared with probabilistic classifier, the possibilistic one is advantageous in reducing the error and confusion between the different classes. Indeed, the statistical assessment of possibilistic result indicates that the overall accuracy is improved from 72.72% to 90.23% and Kappa indicator increases from 0.62 to 0.86.

关键词： Probabilistic logic Possibility theory Accuracy remote sensing Satellites Uncertainty Data models

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Mystery behind similarity measures mse and SSIM

Mystery behind similarity measures mse and SSIM

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IEEE International conference on image processing

作者： Gintautas Palubinskas German Aerospace Center DLR Remote Sensing Technology Institute Oberpfaffenhofen Wessling Germany

Similarity or distance measures play an important role in various pattern recognition applications such as classification, clustering, change detection, information retrieval, energy minimization and optimization problems. We shall analyze theoretically the two most popular quality measures MSE and SSIM used in image processing by showing their origin, similarities/differences and advantages/drawbacks. Both measures depend on the same parameters: sample means, standard deviations and correlation coefficient. It is shown that SSIM originates from two Dice measures and thus inherit their main drawback - dependence on the absolute mean and standard deviation values. Similarly, MSE depends on the absolute standard deviation values. A new similarity measure Composite quality index based on Means, Standard deviations and Correlation coefficient (CMSC) is proposed inheriting advantages of the both measures but at the same time avoiding their drawbacks.

关键词： Correlation coefficient Standards Indexes image quality Time measurement Measurement uncertainty

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Multi-task deep learning for image understanding

Multi-task deep learning for image understanding

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International conference of Soft Computing and pattern recognition (SOCPAR)

作者： Bo Yu Ian Lane Carnegie Mellon University CA Graduate University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Remote Sensing Science Chinese Academy of Sciences Beijing China

Deep learning models can obtain state-of-the-art performance across many speech and image processing tasks, often significantly outperforming earlier methods. In this paper, we attempt to further improve the performance of these models by introducing multi-task training, in which a combined deep learning model is trained for two inter-related tasks. We show that by introducing a secondary task (such as shape identification in the object classification task) we are able to significantly improve the performance of the main task for which the model is trained. Using public datasets we evaluated our approach on two image understanding tasks, image segmentation and object classification. On the image segmentation task, we observed that the multi-task model almost doubled the accuracy of segmentation at the pixel-level (from 18.7% to 35.6%) compared to the single task model, and improved the performance of face-detection by 10.2% (from 70.1% to 80.3%). For the object classification task, we observed a 2.1% improvement in classification accuracy (from 91.6% to 93.7%) compared to a single-task model. The proposed multi-task models obtained significantly higher accuracies than previously published results on these datasets, obtaining 22.0% and 6.2% higher accuracies on the face-detection and object classification tasks respectively. These results demonstrate the effectiveness of multi-task training of deep learning models for image understanding tasks.

关键词： Face Accuracy Three-dimensional displays Data models Face detection Solid modeling Training

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Local Label Probability Propagation for Hyperspectral image Classification

Local Label Probability Propagation for Hyperspectral Image ...

引用

International conference on pattern recognition

作者： Haichang Li Jiangyong Duan Shiming Xiang Lingfeng Wang Chunhong Pan Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479952106

Classification of hyper spectral images is an important issue in remote sensing image processing systems. Hyper spectral images have advantages in pixel-wise classification owing to the high spectral resolution. However, the pixel-wise classification result often introduces the salt-and-pepper appearance because of the complex noise produced by atmosphere and instrument. An effective way to overcome this phenomenon is to resort to the spatial information. This paper proposes a method to solve the above problem by using spatial similarity information. First, in order to avoid the effect of noisy pixels and mixed pixels, reliable seeds are selected in local windows according to the agreement between the central pixel and its spatial neighbors. Then, the information of the reliable seeds is propagated to their spatial neighbors by a graph Laplacian. Specifically, the graph Laplacian is designed to propagate information among spatial neighbors with close similarity relationship so that some small or long thin objects are identified. Through the seed selection and local reliable information propagation, the problem of noisy labels is solved elegantly. Experiments on three real hyper spectral data sets with different spatial resolution, spectral resolution and land covers demonstrate the effectiveness of our method.

关键词： Support vector machines Hyperspectral imaging Reliability Accuracy Vectors Training Noise measurement

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Learning an image-based motion context for multiple people tracking

Learning an image-based motion context for multiple people t...

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IEEE conference on Computer Vision and pattern recognition

作者： Laura Leal-Taixe Michele Fenzi Alina Kuznetsova Bodo Rosenhahn Silvio Savarese Photogrammetry and Remote Sensing ETH Zurich Institute for Information Processing Leibniz University Hannover Computational Vision and Geometry Lab Stanford University

ISBN: (纸本)9781479951192

We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals. At the core of our model lies a learned dictionary of interaction feature strings which capture relationships between the motions of targets. These feature strings, created from low-level image features, lead to a much richer representation of the physical interactions between targets compared to hand-specified social force models that previous works have introduced for tracking. One disadvantage of using social forces is that all pedestrians must be detected in order for the forces to be applied, while our method is able to encode the effect of undetected targets, making the tracker more robust to partial occlusions. The interaction feature strings are used in a Random Forest framework to track targets according to the features surrounding them. Results on six publicly available sequences show that our method outperforms state-of-the-art approaches in multiple people tracking.

关键词： Computer vision image motion analysis Optical imaging Niobium Vegetation Target tracking

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FARNESS PRESERVING NON-NEGATIVE MATRIX FACTORIZATION

FARNESS PRESERVING NON-NEGATIVE MATRIX FACTORIZATION

引用

IEEE International conference on image processing

作者： Mohammadreza Babaee Reza Bahmanyar Gerhard Rigoll Mihai Datcu Institute for Human-Machine Communication Technische Universitat Munchen Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR)

ISBN: (纸本)9781479957521

Dramatic growth in the volume of data made a compact and informative representation of the data highly demanded in computer vision, information retrieval, and pattern recognition. Non-negative Matrix Factorization (NMF) is used widely to provide parts-based representations by factorizing the data matrix into non-negative matrix factors. Since non-negativity constraint is not sufficient to achieve robust results, variants of NMF have been introduced to exploit the geometry of the data space. While these variants considered the local invariance based on the manifold assumption, we propose Farness preserving Non-negative Matrix Factorization (FNMF) to exploits the geometry of the data space by considering non-local invariance which is applicable to any data structure. FNMF adds a new constraint to enforce the far points (i.e., non-neighbors) in original space to stay far in the new space. Experiments on different kinds of data (e.g., Multimedia, Earth Observation) demonstrate that FNMF outperforms the other variants of NMF.

关键词： Non-negative Matrix Factorization Farness Preserving Clustering

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Mean-shift based object detection and clustering from high resolution remote sensing imagery

Mean-shift based object detection and clustering from high r...

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4th National conference on Computer Vision, pattern recognition, image processing and Graphics (NCVPRIPG)

作者： SushmaLeela, T. chandrakanth, R. Saibaba, J. Varadan, Geeta Mohan, Sambhu S. ADRIN Department of Space Hyderabad India Amrita Viswa Vidyapeetham Amrita School of Engineering Coimbatore India

ISBN: (纸本)9781479915880

Object detection from remote sensing images has inherent difficulties due to cluttered backgrounds and noisy regions from the urban area in high resolution images. Detection of objects with regular geometry, such as circles from an image uses strict feature based detection. Using region based segmentation techniques such as K-Means has the inherent disadvantage of knowing the number of classes apriori. Contour based techniques such as Active contour models, sometimes used in remote sensing also has the problem of knowing the approximate location of the region and also the noise will hinder its performance. A template based approach is not scale and rotation invariant with different resolutions and using multiple templates is not a feasible solution. This paper proposes a methodology for object detection based on mean shift segmentation and non-parametric clustering. Mean shift is a non-parametric segmentation technique, which in its inherent nature is able to segment regions according to the desirable properties like spatial and spectral radiance of the object. A prior knowledge about the shape of the object is used to extract the desire object. A hierarchical clustering method is adopted to cluster the objects having similar shape and spatial features. The proposed methodology is applied on high resolution EO images to extract circular objects. The methodology found to be better and robust even in the cluttered and noisy background. The results are also evaluated using different evaluation measures.

关键词： mean shift PAMS segmentation shape features clustering Agglomerative

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remote sensing Object recognition Based on Transfer Learning

Remote Sensing Object Recognition Based on Transfer Learning

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10th International conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Dan, Zhiping Sang, Nong Chen, Yanfei Chen, Xi Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9781467352536

The deviation of an object's real data distribution from the known training data distribution would lead to low reliability of object recognition. To tackle this problem for remote sensing (RS) images, we propose a novel object recognition method based on transfer learning. The feature vectors of an object are first extracted by a joint Local Binary pattern (LBP). The transfer learning is then employed to find the common parameter set among feature spaces of the object under different distributions. Through extensive experiments, it has been shown that a significant improvement on the accuracy is has been brought by the proposed novel method.

关键词： object recognition remote sensing image processing machine learning

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