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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311-320 订阅

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Novel change detection method based on direction feature and fuzzy clustering for remote sensing images and its application in biological invasions

Novel change detection method based on direction feature and...

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2013 International conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2013

作者： Li, Qingsong Qin, Xizhong Jia, Zhenhong Yang, Jie Hu, Raphael College of Information Science and Engineering Xinjiang University 830046 Urumqi China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 200240 Shanghai China Knowledge Engineering and Discovery Research Institute Auckland University of Technology 1020 Auckland New Zealand

ISBN: (纸本)9783642419072

Change detection techniques attempt to be used for remote sensing monitoring of invasive plants. A novel change detection method based on direction feature and RFLICM (an improved fuzzy C-means clustering) is proposed. Firstly, the difference image is acquired from multitemporal images. The pixel values of the difference image are updated using the template of directional neighborhood based on direction feature. The final change detection map is achieved by clustering the pixel values of the difference image using RFLICM algorithm into two disjoint classes: changed and unchanged. The results obtained by experiment are compared with some other existing state of the art methods. It is observed that the proposed method outperforms the other methods. Finally, the proposed change detection technique is applied to remote sensing monitoring of invasive plants. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Change detection

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PIAGENG 2013: image processing and Photonics for Agricultural Engineering

PIAGENG 2013: Image Processing and Photonics for Agricultura...

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3rd International conference on Photonics and image in Agriculture Engineering, PIAGENG 2013

ISBN: (纸本)9780819495587

The proceedings contain 45 papers. The topics discussed include: application of laser speckle pattern analysis for plant sensing;research on animation design of growing plant based on 3D max technology;a novel robust digital image watermarking using LPM and HVS;research on spatial coding compressive spectral imaging and its applicability for rural survey;dynamic visual image modeling for 3D synthetic scenes in agricultural engineering;MRF model with adaptive multiresolution for image segmentation;halo performance on low light level image intensifiers;an efficient license plate character recognition algorithm based on shape context;fish freshness rapid detection based on fish-eye image;a new image fusion technology based on object extraction and NSCT;and monitoring land coverage change in mining area by remote sensing image classification.

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Influence of clique potential parameters on classification using Bayesian MRF model for remote sensing image in Dali Erhai basin

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2012 International conference on Materials and Manufacturing Research, ICMMR 2012

作者： Duan, Haijun Wu, Guangmin Liu, Dan Mai, John D. Chen, Jianming MEMS and Pattern Recognition Lab Kunming Uni. of Sci. and Tech 650093 Kunming China Dept. of Mechanical and Biomedical Engineering City University of Hong Kong Hong Kong

ISBN: (纸本)9783037856192

image classification of remote sensing data is an important topic and long-term tasks in applications [1]. Markov random field (MRF) has more advantages in processing contextual information [2]. Bayesian approach enables the incorporation of prior model and likelihood distribution, this paper has formulated a Bayesian-MRF classification model based on MAP-ICM framework. It uses Potts model in label field and assume Gaussian distribution in observation field. According to maximum a posteriori (MAP) criterion, each new classified label can be obtained by the minimum of energy using Iterated Conditional Modes (ICM) algorithm. Finally, classification tasks are carried out by Bayesian-MRF classification model. Experimental results show that: (1) Clique potential parameters affect classification greatly. When it is 0.5, the classification accuracy reaches maximum with the best classification result for study area of Dali Erhai Lake basin using landsat TM data. (2) Bayesian MRF model have obvious advantages in classification for neighbourhood pixels so that it can separate Shadow class from Water class because the Shadow in mountain areas is very similar to Water in spectrum. In this case study, the best classification accuracy reaches 95.8%. The approaches and results will have important reference value for applications such as land use/cover classification, environment/ecological monitoring etc. © (2013) Trans Tech Publications, Switzerland.

关键词： Landsat

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

作者： SushmaLeela T Chandrakanth R Saibaba J Geeta Varadan Sambhu.S. Mohan ADRIN Department of Space Hyderabad India Amrita School of Engineering Bangalore Karnataka IN

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： image segmentation Feature extraction Shape remote sensing Object recognition Spatial resolution

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Fusion of satellite images using Compressive Sampling Matching Pursuit (CoSaMP) method

Fusion of satellite images using Compressive Sampling Matchi...

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

作者： B. Sathyabama S.G. Siva Sankari S. Nayagara Department of ECE Thiagarajar college of Engineering Madurai

Fusion of Low Resolution Multi Spectral (LRMS) image and High Resolution Panchromatic (HRPAN) image is a very important topic in the field of remote sensing. This paper handles the fusion of satellite images with sparse representation of data. The High resolution MS image is produced from the sparse, reconstructed from HRPAN and LRMS images using Compressive Sampling Matching Pursuit (CoSaMP) based on Orthogonal Matching Pursuit (OMP) algorithm. Sparse coefficients are produced by correlating the LR MS image patches with the LR PAN dictionary. The HRMS is formed by convolving the Sparse coefficients with the HR PAN dictionary. The world view -2 satellite images (HRPAN and LRMS) of Madurai, Tamil Nadu are used to test the proposed method. The experimental results show that this method can well preserve spectral and spatial details of the input images by adaptive learning. While compared to other well-known methods the proposed method offers high quality results to the input images by providing 87.28% Quality with No Reference (QNR).

关键词： Matching pursuit algorithms Dictionaries image fusion image resolution remote sensing image reconstruction Indexes

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image fusion using spatial unmixing

Image fusion using spatial unmixing

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International conference on Signal processing, image processing and pattern recognition (SIP)

作者： S. Arivazhagan J. Praislin Anisha Mepco Schlenk Engineering College Sivakasi India

In remote sensing applications, image acquired from space borne satellites are of diverse spatial, spectral and temporal resolutions. Several situations in image interpretation require high spatial information and high spectral information in a single image. But the existing sensors do not have the ability to provide such information either by design or because of observational constraints. Thus the complementary information from different sensors are integrated using the technique called image fusion to get a resultant fused image which is more informative than any of the given input images. The objective of this work is to perform image fusion on a high spatial resolution LISS IV image (with low spectral resolution) and a high spectral resolution LISS iiI image (with low spatial resolution) to obtain a fused image with high spatial resolution and high spectral resolution. To find the endmember proportions of the mixed pixels, the land cover map is obtained by performing unsupervised soft classification on the high spatial resolution LISS IV image. The land cover class proportions thus obtained is downscaled to get the land cover class proportions at LISS iiI scale. The unmixing algorithm used here is spatial unmixing wherein the high spectral resolution LISS iiI image is processed using a sliding window approach to obtain the endmember spectra. Thus the pixels of the fused image are obtained as the linear combination of endmembers derived from the LISS iiI weighted by the land cover class proportions of LISS IV.

关键词： Spatial resolution remote sensing Satellites Cameras Earth

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Multi-spectral demosaicing technique for single-sensor imaging

Multi-spectral demosaicing technique for single-sensor imagi...

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

作者： Hemant Kumar Aggarwal Angshul Majumdar Indraprastha Institute of Information Technology-Delhi India

A generic-filter array design have been proposed to capture multi-spectral images using hypothetical single-sensor multi-spectral cameras. The design idea is based on uniform sampling of intensity values from each band irrespective of spectral properties of any particular band. A reconstruction technique have also been proposed to linearly interpolate unknown intensity values of other bands at each pixel. Proposed technique was evaluated using two multispectral image datasets where one was of Landsat satellite and another was of cooled CCD camera Apogee Alta U260. Quantitative evaluation of the proposed technique was done using peak signal to noise ratio.

关键词： Interpolation Cameras Arrays PSNR image reconstruction Satellites remote sensing

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remote sensing image retrieval using morphological texture descriptors

Remote sensing image retrieval using morphological texture d...

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IEEE Signal processing and Communications Applications (SIU)

作者： Erchan Aptoula Semih Korkmaz Bilgisayar Mühendisliği Bölümü Okan Üniversitesi Istanbul Turkey

This paper presents the results of applying morphological texture descriptors to the problem of content-based retrieval of remote sensing images. Mathematical morphology offers a variety of multi-scale texture descriptors, capable of computing translation, rotation and illumination invariant features. In particular, we focus on the circular covariance histogram and the rotation invariant points approaches, and test them with the UC Merced Land Use dataset. They are compared against other known descriptors such as LBP and Gabor filters, and are shown to provide either comparable or superior performance despite their shorter feature vector length.

关键词： Histograms remote sensing Satellites image retrieval Content-based retrieval Morphology pattern recognition

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remote sensing Object recognition Based on Transfer Learning

Remote Sensing Object Recognition Based on Transfer Learning

引用

International conference on Fuzzy Systems and Knowledge Discovery

作者： Zhiping Dan Nong Sang Yanfei Chen Xi Chen School of Automation Huazhong University of Science and Technology

ISBN: (纸本)9781467352512

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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Implementation of image Fusion algorithm using MATLAB (LAPLACIAN PYRAMID)

Implementation of Image Fusion algorithm using MATLAB (LAPLA...

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International Multi-conference on Automation, Computing, Communication, Control and Compressed sensing

作者： M. Pradeep Assoc. Professor ECE Department Shri Vishnu Engineering College for Women Bhimavaram India

ISBN: (纸本)9781467350891

This paper represents a approach to implement image fusion algorithm ie LAPLACIAN PYRAMID. In this technique implements a pattern selective approach to image fusion. The basic idea is to perform a pyramid decomposition on each source image and finally reconstruct the fused image by performing an inverse pyramid transform. It offers benefits like resolution, S/N ratio and pixel size. The aim of image fusion, apart from reducing the amount of data, is to create new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging Based on this technique finally it reconstructs the fused image from the fused pyramid.

关键词： image fusion Laplacian Pyramid image fusion system

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