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

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A genetic algorithm based method to improve image segmentation

A genetic algorithm based method to improve image segmentati...

引用

International conference on pattern recognition

作者： A. Visa Department of Information Science Lappeenranta University of Technology Lappeeranta Finland

Segmentation of textured images is becoming more and more important in applications, as quality control or remote sensing. Segmentation of textured images is demanding. A new genetic algorithm based method to post-process segmented texture images is presented. A genetic algorithm is used to extract web-like rules from segmented texture images. These rules are checked and they are used in post-processing to improve the segmentation. An unsupervised image segmentation and definition of classes by class prototypes are assumed. Some preliminary results are presented.

关键词： Genetic algorithms image segmentation Prototypes Quality control remote sensing Application software Stochastic resonance Relaxation methods Information science image processing

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A remote sensing data fusion approach to monitor agricultural areas

A remote sensing data fusion approach to monitor agricultura...

引用

14th International conference on pattern recognition, ICPR 1998

作者： Bouzidi, Sonia Berroir, Jean-Paul Herlin, Isabelle INRIA BP 105 78153 Le Chesnay Cedex France

ISBN: (纸本)0818685123

Describes a fusion process between two different data sources, one providing an accurate spatial information, the other providing time series with a much coarser spatial scale. It is applied in the following remote sensing context: the forecast of cereals production, which is a challenging application of the new generation of Earth observation satellites. These two data types are required since agronomical models must be fed with a daily sampling of cereals reflectances, and since in Europe, fields have a relatively small size. SPOT-XS is wed to provide spatial information at the parcels level, a meso-scale sensor (here, NOAA-AVHRR), which outputs images of large areas every day, provides the temporal information. The combination of these two data sources makes it possible to daily estimate reflectances of main cultivations at the parcels level. The selected approach is as follows: a preliminary learning stage provides the reflectances of each type of cultivation;then operational scenarios are defined to apply the learning information in order to estimate statistics on large areas: using only one SPOT-XS image and meso-scale daily images, a fusion scheme makes it possible to obtain land use identification at high spatial resolution with its temporal behavior. © 1998 IEEE.

关键词： Reflection

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Methods for classification of agricultural fields in aerial sequences -: a comparative study

Methods for classification of agricultural fields in aerial ...

引用

conference on remote sensing for Agriculture, Ecosystems, and Hydrology

作者： Houkes, Z Chen, HJ Blacquière, JF Univ Twente Fac Elect Engn NL-7500 AE Enschede Netherlands

ISBN: (纸本)0819429589

A comparative study of a selection of classification methods for agricultural fields in sequences of aerial images is presented. The image sequences are acquired by an RGB-CCD video camera which is assumed to be on board of an airplane, moving linear over the scene. The objects in the scenes being considered are agricultural fields. The classes of agricultural fields to be distinguished are determined by the type of crop, e.g. potatoes, sugar beet, wheat, etc. In order to recognize and classify these fields obtained from the aerial sequences of images, a common approach is in the use of surface texture. Textural features are extracted from the images to effectively characterize the vegetation. Methods based on Circular Symmetric Auto-Regression, Go-Occurrence Matrix and Local Binary patterns are selected for the comparative study. The experiments are carried out with image sequences taken from a scaled model of a landscape and a selection from the Brodatz set. A few training images are used to set up the model bases for the three methods. The methods are tested using the same regions from other images of the sequence, and other sequences of images of similar fields. Comparison of the methods is based on the confusion matrix. Sensitivity to variations in flight direction, variations in altitude and luminance conditions are being considered.

关键词： texture classification vegetation agricultural fields aerial images pattern recognition

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Unsupervised texture segmentation for multispectral remote-sensing images

Unsupervised texture segmentation for multispectral remote-s...

引用

International conference on pattern recognition

作者： Din-Chang Tseng Hung-Ming Tsai Chih-Ching Lai Institute of Computer Science and Information Engineering National Central University Chungli Taiwan

ISBN: (纸本)0818685123

An unsupervised texture segmentation approach for multispectral remote-sensing images is proposed. Firstly, a scale-space filter (SSF) based histogram thresholding is used to threshold each spectrum space of a multispectral remote-sensing image to detect the major clusters of the multispectral data to generate the principal multispectrum set. Secondly, a GMRF (Gaussian Markov random field) is used to model the multispectral texture image, then the global GMRF parameters in a posteriori distribution probability are estimated. We label each pixel of the image based on the principal multispectrum set and the global GMRF parameters to maximize a posteriori distribution probability (MAP). Thirdly, a uniformity criterion is presented to each pixel in the global segmented image to determine whether it should be estimated the local MRF parameters or not. A max-min distance clustering method is then used to cluster the estimated local MRF parameters to further segment the image. Several remote-sensing images were processed by the proposed approach to demonstrate the segmentation ability.

关键词： image segmentation remote sensing Histograms Pixel Clustering methods Markov random fields Parameter estimation Multispectral imaging Spatial resolution Computer science

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Improved JPEG coding for remote sensing

Improved JPEG coding for remote sensing

引用

International conference on pattern recognition

作者： P. Hou A. Hojjatoleslami M. Petrou C. Underwood J. Kittler School of Electronic Engineering Information Technology and Mathematics University of Surrey UK

In this paper we propose two improvements to standard JPEG coding that can improve the compression ratio achieved for compressing remote sensing images obtained by sensors on-board microsatellites by more than 39-60%. The first improvement consists in using a quantisation table that has been shown experimentally to be more appropriate for remote sensing images. The second and more significant improvement comes from the use of a novel region growing algorithm that can identify the outer border of a cloud region. The blocks that correspond to cloud regions are subsequently smoothed, as they represent unwanted information for the applications we are interested in, and encoded. The results are demonstrated with the help of several real images obtained by the Surrey University satellites.

关键词： remote sensing image coding Transform coding Clouds Earth Quantization Sea surface Information technology Mathematics image sensors

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A basis function network for remote sensing classification

A basis function network for remote sensing classification

引用

International conference on Signal processing Proceedings (ICSP)

作者： Qiu Bensheng Gao Yuangui Yang Jingan Zhang Diancheng People''s Liberation Army General Hospital Beijing China Hefei University of Technology China

For the problem of remote sensing image classification, a basis function network is proposed in this paper. Compared with the backpropagation (BP) network, it avoids the problems of hidden nodes selection and weights initialization. The experimental results show that this basis function network has a better performance of classification for complex remote sensing patterns with fast training time.

关键词： remote sensing pattern classification Programmable logic arrays Hospitals Neural networks image processing Biology computing Computer errors Color Artificial neural networks

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A fast finding and fitting algorithm to detect circles

A fast finding and fitting algorithm to detect circles

引用

IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Wei Yi Laboratory For Information Engineering. in Surveying Mapping & Remote Sensing Wuhan Technical University of Surveying and Mapping Wuhan China

ISBN: (纸本)0780344030

Fast finding of circles in images by using the geometric symmetry of the circle and the division of circles into different subimages happen at the same time. Then for each subimage, a fitting algorithm is conducted to estimate three parameters of a circle. Fully proved by experiments, this method possesses superiority in speed, memory usage and precision as compared with other methods now available.

关键词： Parameter estimation Estimation theory pattern recognition Convergence remote sensing Fitting image recognition Digital images image processing Aerospace industry

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A remote sensing data fusion approach to monitor agricultural areas

A remote sensing data fusion approach to monitor agricultura...

引用

International conference on pattern recognition

作者： S. Bouzidi J.-P. Berroir I. Herlin INRIA Le Chesnay France

Describes a fusion process between two different data sources, one providing an accurate spatial information, the other providing time series with a much coarser spatial scale. It is applied in the following remote sensing context: the forecast of cereals production, which is a challenging application of the new generation of Earth observation satellites. These two data types are required since agronomical models must be fed with a daily sampling of cereals reflectances, and since in Europe, fields have a relatively small size. SPOT-XS is wed to provide spatial information at the parcels level, a meso-scale sensor (here, NOAA-AVHRR), which outputs images of large areas every day, provides the temporal information. The combination of these two data sources makes it possible to daily estimate reflectances of main cultivations at the parcels level. The selected approach is as follows: a preliminary learning stage provides the reflectances of each type of cultivation; then operational scenarios are defined to apply the learning information in order to estimate statistics on large areas: using only one SPOT-XS image and meso-scale daily images, a fusion scheme makes it possible to obtain land use identification at high spatial resolution with its temporal behavior.

关键词： remote monitoring Satellites Vegetation mapping Earth Europe Statistics Frequency estimation remote sensing Production image sensors

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Identification of airfield runways in synthetic aperture radar images

Identification of airfield runways in synthetic aperture rad...

引用

International conference on pattern recognition

作者： I. Finch A. Antonacopoulos Department of Computer Science University of Liverpool Liverpool UK University of Liverpool Liverpool Merseyside GB

Synthetic aperture radar (SAR) is a microwave-based remote sensing technique whereby images can be captured when optical images cannot, at night or when there is cloud cover. However, its very low signal-to-noise ratio (1:1) means that conventional image analysis techniques are unsuitable for SAR imagery. This paper presents a novel approach to the detection of very small objects in SAR imagery, which combines a preprocessing stage with a Hough transform analysis stage using contextual information to identify suitable 'signatures'. The result of this procedure is the fast and accurate identification of airfield runways. The method is based on the fact that the only reliable characteristic of airfield runways visible in SAR images is the location of lights along their sides.

关键词： image analysis Synthetic aperture radar Microwave theory and techniques remote sensing Adaptive optics Laser radar Optical sensors Clouds Signal to noise ratio Object detection

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Semi-automated Maltese front position determination

Semi-automated Maltese front position determination

引用

International conference on pattern recognition

作者： S.M. Lea M. Lybanon Department of Mathematical Sciences University of North Carolina Greensboro Greensboro NC USA Remote Sensing Applications Branch Stennis Space Center Naval Research Laboratory Inc. MS USA

The authors investigate the reliability of a semi-automated system designed to locate the Maltese front from satellite AVHRR and SAR imagery. The opening and closing operations of mathematical morphology afford a means of image segmentation that provides smooth, strong, continuous edges except where the edges are obscured by clouds or similar phenomena. The authors evaluate the results by comparing them with front positions found by experienced Navy analysts. Their system provides an objective method for front location that is less labor-intensive than manual methods currently in use.

关键词： Ocean temperature Sea surface Satellite broadcasting Sea surface roughness Rough surfaces Surface roughness Predictive models Synthetic aperture radar remote sensing Laboratories

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