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International conference on Communication Technology (ICCT)

作者： Qiang Zhang Xiaorun Li College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781467370066

images acquired in bad weather, such as haze and fog, are seriously degraded by the scatting of the atmosphere. In this paper, a novel fast image dehazing method using guided filter is proposed. Before guided filter processing, the rough transmission map and the guidance image employ nearest neighbor interpolation down-sampling, to zoom out on the input images of guided filter and reduce the processing of guided filter time complexity. After guided filter processing, the guided filter output image employs bilinear interpolation up-sampling, to zoom in on the output image of guided filter and get the refining transmission map. Results demonstrate that the proposed method achieves good dehazing effect as well as speeds up the dehazing processing. With the improvements, the proposed method may be applied in video surveillance, intelligent transportation system and remote sensing.

关键词： Artificial intelligence Interpolation image edge detection pattern recognition

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An improved LBP transfer learning for remote sensing object recognition

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OPTIK 2014年第1期125卷 482-485页

作者： Dan, Zhiping Sang, Nong He, Yonggang Sun, Shuifa China Three Gorges Univ Inst Intelligent Vis & Image Informat Yichang Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

In the object recognition process of the remote sensing image, as the object data is influenced by the imaging scale, intensity and the shape of the object, the distribution of the object data is very different with that of known training data which leads to the low reliability of the object recognition. Aimed at the problem, an object recognition method based on transfer learning framework for the remote sensing image is proposed in this paper. The feature vectors of the object data are extracted by an improved LBP firstly, and then the transfer learning is used to find the common parameters among the feature spaces of the object data with the different distributions. The transfer learning method can transfer knowledge from the old object data to the new object data and improve the performance of the object recognition. According to the experiments in the satellite remote sensing images, it shows that the accuracy of object recognition has been greatly improved by our proposed method compared with the other classical methods. (C) 2013 Elsevier GmbH. All rights reserved.

关键词： Sensors applications Object recognition image processing Machine learning

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Extraction of hazelnut fields using Approximate Spectral Clustering Ensemble

Extraction of hazelnut fields using Approximate Spectral Clu...

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IEEE Signal processing and Communications Applications (SIU)

作者： Berna Yalçın Yaser Moazzen Kadim Taşdemir Istanbul Teknik Universitesi Istanbul TR Elektronik ve Haberleşme Mühendisliği Bölümü İstanbul Teknik Üniversitesi İstanbul Türkiye Bilgisayar Mühendisliği Bölümü Uluslararası Antalya Üniversitesi Antalya Türkiye

Considering the economic and environmental aspects, hazelnut orchards are of great importance in Turkey. It is crucial to develop methods for detecting, monitoring, protecting and managing these orchards. This can be done exactly and fast by evaluating the remote sensing images of these areas. For this aim clustering methods are so popular due to their unsupervised nature. Particularly, spectral clustering can be useful thanks to its ability to extract clusters of different structures and its easy implementation. However, its direct use in remote sensing images is infeasible due to its high computational and memory cost. That's why its indirect implementation, which is named approximate spectral clustering (ASC), is applied on quantized or sampled prototypes of the image. ASC not only takes advantages of spectral clustering but also utilizes different information types (such as topology and density) on the prototype level for effective similarity definition. Although ASC can be used with various similarity measures, the optimum measure depends on the approximation method (sampling/quantization), dataset and application. Hence, in this study, by introducing Selective Sampling based Approximate Spectral Clustering Ensemble method (SSASCE), we eliminate the need to determine the optimum measure and harness their individual advantages. We then show the outperformance of SSASCE on extraction of hazelnut fields.

关键词： Approximation methods pattern recognition remote sensing image segmentation Prototypes Topology

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Improving Spatial Feature Representation from Aerial Scenes by Using Convolutional Networks

Improving Spatial Feature Representation from Aerial Scenes ...

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Brazilian Symposium on Computer Graphics and image processing (SIBGRAPI)

作者： Keiller Nogueira Waner O. Miranda Jefersson A. Dos Santos Universidade Federal de Minas Gerais Belo Horizonte Brazil Dept. of Comput. Sci. Univ. Fed. de Minas Gerais Belo Horizonte Brazil

The performance of image classification is highly dependent on the quality of extracted features. Concerning high resolution remote image images, encoding the spatial features in an efficient and robust fashion is the key to generating discriminatory models to classify them. Even though many visual descriptors have been proposed or successfully used to encode spatial features of remote sensing images, some applications, using this sort of images, demand more specific description techniques. Deep Learning, an emergent machine learning approach based on neural networks, is capable of learning specific features and classifiers at the same time and adjust at each step, in real time, to better fit the need of each problem. For several task, such image classification, it has achieved very good results, mainly boosted by the feature learning performed which allows the method to extract specific and adaptable visual features depending on the data. In this paper, we propose a novel network capable of learning specific spatial features from remote sensing images, with any pre-processing step or descriptor evaluation, and classify them. Specifically, automatic feature learning task aims at discovering hierarchical structures from the raw data, leading to a more representative information. This task not only poses interesting challenges for existing vision and recognition algorithms, but also brings huge opportunities for urban planning, crop and forest management and climate modelling. The propose convolutional neural network has six layers: three convolutional, two fully-connected and one classifier layer. So, the five first layers are responsible to extract visual features while the last one is responsible to classify the images. We conducted a systematic evaluation of the proposed method using two datasets: (i) the popular aerial image dataset UCMerced Land-use and, (ii) a multispectral high-resolution scenes of the Brazilian Coffee Scenes. The experiments show that the pro

关键词： Feature extraction Neurons remote sensing Mathematical model Biological neural networks Visualization Robustness

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ROAD EXTRACTION VIA ADAPTIVE GRAPH CUTS WITH MULTIPLE FEATURES

ROAD EXTRACTION VIA ADAPTIVE GRAPH CUTS WITH MULTIPLE FEATUR...

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IEEE International conference on image processing

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

ISBN: (纸本)9781479983407

Accurate road extraction from complex backgrounds plays a fundamental role in a wide range of remote sensing applications. There are two shortcomings for the existing methods: 1) Most of them ignore the spatially contextual information inherent in images;2) Few existing methods show robustness to the occlusions of cars or trees. To address these two problems, we propose a novel approach via adaptive graph cuts with multiple features. Specifically, for the former problem, we apply multiple features (spectral feature, spatial feature and gradient feature) to obtain not only the spectral characteristic but also the spatially contextual feature. In this way, the structural information of road network can be effectively captured. For the latter, adaptive graph cuts based algorithm is adopted. These two schemes show better performance than state-of-the-art methods under the conditions of occlusions. Experiments on 25 images indicate the validity and effectiveness of our method by comparing with state-of-the-art approaches.

关键词： Road extraction Adaptive graph cuts Multiple features Spatially contextual feature

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Multivalued component-tree filtering 22

Multivalued component-tree filtering

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22nd International conference on pattern recognition (ICPR)

作者： Kurtz, Camille Naegel, Benoit Passat, Nicolas Univ Paris 05 LIPADE Paris France Univ Strasbourg CNRS ICube Strasbourg France Univ Reims Reims France

ISBN: (纸本)9781479952083

We introduce the new notion of multivalued component-tree, that extends the classical component-tree initially devoted to grey-level images, in the mathematical morphology framework. We prove that multivalued component-trees can model images whose values are hierarchically organized. We also show that they can be efficiently built from standard component-tree construction algorithms, and involved in antiextensive filtering procedures. The relevance and usefulness of multivalued component-trees is illustrated by an applicative example on hierarchically classified remote sensing images.

关键词： Computational efficiency image processing Level set Mathematical model Morphology remote sensing Standards

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Spectral Fidelity Analysis of Compressed sensing Reconstruction Hyperspectral remote sensing image Based on Wavelet Transformation 6

Spectral Fidelity Analysis of Compressed Sensing Reconstruct...

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6th Chinese conference on pattern recognition (CCPR)

作者： Ma, Yi Zhang, Jie An, Ni State Ocean Adm Inst Oceanog 1 Qingdao Peoples R China

ISBN: (纸本)9783662456439;9783662456422

For hyperspectral image research, spectral characteristic retainment is more important than the spatial details retainment, so it is necessary to evaluate the spectral influence of hyperspectral image compressed sensing. In this paper, the researchers select a hyperspectral remote sensing image PROBE CHRIS with abundant coastal wetland ground objects to analyze spectral fidelity of wavelet transform compressed sensing algorithm on the basis of three indicators between reconstruction and original image pixel spectra: correlation coefficient, error and relative error. Meanwhile, eight typical ground objects are chosen to analyze their respective spectral deviation. The results indicate: (1) image reconstruction algorithm based on wavelet transform compressed sensing functions well. Between the pixels of reconstruction image and original one, their average spectral correlation coefficient is 0.9428, error is 6.4096, and relative error is 13.81%;(2) Spectrum fidelity indicator values vary with wavebands. Reconstruction algorithm is selective about objects.

关键词： Spectral fidelity compressed sensing reconstruction hyperspectral remote sensing image

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pattern recognition: Advanced Development, Techniques and Application for image Retrieval

Pattern Recognition: Advanced Development, Techniques and Ap...

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International conference on Communication and Network Technologies (ICCNT)

作者： Khodaskar, A. A. Ladhake, S. A. SIPNA COET Comp Sci & Engn Amravati India

ISBN: (纸本)9781479962662

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： algorithms agriculture biometrics life form analysis medical diagnosis pattern recognition techniques perceptual capability recognition intelligence robotics

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Intermediate Grouping on remotely Sensed Data Using Gestalt Algebra 20

Intermediate Grouping on Remotely Sensed Data Using Gestalt ...

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conference on image and Signal processing for remote sensing XX

作者： Michaelsen, Eckart Fraunhofer IOSB D-76275 Ettlingen Germany

ISBN: (纸本)9781628413076

Human observers often achieve striking recognition performance on remotely sensed data unmatched by machine vision algorithms. This holds even for thermal images (IR) or synthetic aperture radar (SAR). Psychologists refer to these capabilities as Gestalt perceptive skills. Gestalt Algebra is a mathematical structure recently proposed for such laws of perceptual grouping. It gives operations for mirror symmetry, continuation in rows and rotational symmetric patterns. Each of these operations forms an aggregate-Gestalt of a tuple of part-Gestalten. Each Gestalt is attributed with a position, an orientation, a rotational frequency, a scale, and an assessment respectively. Any Gestalt can be combined with any other Gestalt using any of the three operations. Most often the assessment of the new aggregate-Gestalt will be close to zero. Only if the part-Gestalten perfectly fit into the desired pattern the new aggregate-Gestalt will be assessed with value one. The algebra is suitable in both directions: It may render an organized symmetric mandala using random numbers. Or it may recognize deep hidden visual relationships between meaningful parts of a picture. For the latter primitives must be obtained from the image by some key-point detector and a threshold. Intelligent search strategies are required for this search in the combinatorial space of possible Gestalt Algebra terms. Exemplarily, maximal assessed Gestalten found in selected aerial images as well as in IR and SAR images are presented.

关键词： Perceptual grouping Gestalt machine vision remote sensing object recognition

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Local Label Probability Propagation for Hyperspectral image Classification 22

Local Label Probability Propagation for Hyperspectral Image ...

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22nd International conference on pattern recognition (ICPR)

作者： Li, Haichang Duan, Jiangyong Xiang, Shiming Wang, Lingfeng Pan, Chunhong Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9781479952083

Classification of hyperspectral images is an important issue in remote sensing image processing systems. Hyperspectral 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 hyperspectral data sets with different spatial resolution, spectral resolution and land covers demonstrate the effectiveness of our method.

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

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