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Removing vegetation using unsupervised fully constrained least squares linear spectral mixture analysis method in soils surveying by remote sensing

Removing vegetation using unsupervised fully constrained lea...

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conference on image processing and pattern recognition in remote sensing II

作者： Luo, HX Ye, HZ Ke, YH Pan, JP Gong, JY Chen, XL SW Normal Univ Sch Resources & Environm Chongqing 400715 Peoples R China

ISBN: (纸本)0819456187

In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed sensor with given spatial resolution are a mixture of soil and vegetation spectra, so vegetation covering on soil influences the accuracy of soils surveying by remote sensing. Mixed pixel spectra are described as a linear combination of endmember signature matrix with appropriate abundance fractions correspond to it in a linear mixture model. According to the principle of this model, abundance fractions of endmembers in every pixel were calculated using unsupervised fully constrained least squares(UFCLS) algorithm. Then the signature of vegetation correspond to its abundance fraction was eliminated, and other endmember signatures covered by vegetation were restituted by scaling their abundance fractions to sum the original pixel total and recalculating the model. After above processing, de-vegetated reflectance images were produced for soils surveying. The accuracies of paddy soils classified using these characteristic images were better than that of using the raw images, but the accuracies of zonal soils were inferior to the latter. Compared to many other image processing methods, such as K-T transformation and ratio bands, the linear spectral unmixing to removing vegetation produced slightly better overall accuracy of soil classification, so it was useful for soils surveying by remote sensing.

关键词： soils surveying classification of remote sensing image linear spectral mixture model fully constrained least squares (FCLS) algorithm unsupervised FCLS (UFCLS) method

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IEEE1394 and entropy reduction techniques as applied to remote sensing spacecraft communication systems

IEEE1394 and entropy reduction techniques as applied to remo...

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conference on image processing and pattern recognition in remote sensing II

作者： Masini, P Julian, RL Jensen, KL Raytheon Co. (United States)

ISBN: (纸本)0819456187

This paper describes work being done at Raytheon-Santa Barbara remote sensing (SBRS) in the area of entropy reduction of remote sensing data on the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Visible/Infrared imager/Radiometer Suite (VIIRS) instrument. The VIIRS instrument will produce the largest amount of data on the NPOESS satellite platform, and thus has the greatest impact on data rate. The VIIRS instrument produces 22 bands of radiometric and imaging data, which must be transmitted to the spacecraft without loss of data integrity. VIIRS uses an implementation of the RICE algorithm, along with spectral subtraction and data trimming that are described in this paper, to provide lossless data compression. This paper will also describe a simulation that predicted the data reduction performance and the resulting sensor data rates when VIIRS observes the earth from orbit. This paper will also describe the VIIRS implementation of the Fault Tolerant 1394 data network that utilizes the 1394 ASIC chipset developed by the NPOESS Integrated Program Office (IPO) and Northrop Grumman Space and Technology (NGST). This high-speed network will facilitate the reliable transmission of large amounts of compressed and uncompressed science and telemetry data from the VIIRS instrument to the NPOESS spacecraft.

关键词： Sensors Space operations Electronics Data compression Data processing Satellites MODIS remote sensing Simulation of CCA and DLA aggregates Staring arrays

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Implementation of real-time constrained linear discriminant analysis to remote sensing image classification

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pattern recognition 2005年第4期38卷 459-471页

作者： Du, Q Nekovei, R Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX 78363 USA

In this paper, we investigate the practical implementation issues of the real-time constrained linear discriminant analysis (CLDA) approach for remotely sensed image classification. Specifically, two issues are to be resolved: (1) what is the best implementation scheme that yields lowest chip design complexity with comparable classification performance. and (2) how to extend CLDA algorithm for multispectral image classification. Two limitations about data dimensionality have to be relaxed. One is in real-time hyperspectral image classification. where the number of linearly independent pixels received for classification must be larger than the data dimensionality (i.e., the number of spectral bands) in order to generate a non-singular sample correlation matrix R for the classifier, and relaxing this limitation can help to resolve the aforementioned first issue. The other is in multispectral image classification. where the number of classes to be classified cannot be greater than the data dimensionality, and relaxing this limitation can help to resolve the afore mentioned second issue. The former can be solved by introducing a pseudo inverse initiate of sample correlation matrix for R-1 adaptation. and the latter is taken care, of by expanding the data dimensionality via the operation of hand multiplication. Experiments on classification performance using these modifications are conducted to demonstrate their feasibility. All these investigations lead to a detailed ASIC chip design-scheme for the real-time CLDA algorithm suitable to both hyperspectral and multispectral images. The proposed techniques. to resolving these two dimensionality limitations are instructive to the real-time implementation of several popular detection and classification approaches in remote sensing image exploitation. (C) 2004 pattern recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： real-time processing constrained linear discriminant analysis (CLDA) classification hyperspectral imagery imagery

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recognition of image with natural textures based on learning of information augmentation

Recognition of image with natural textures based on learning...

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2nd International conference on Intelligent Information processing

作者： Cheng, XY Yuan, XH Li, SQ Xian, DS Nanjing Univ Sci & Technol Dept Comp Sci Nianjing 21904 Peoples R China

ISBN: (纸本)038723151X

The efficiency of pattern recognition depends heavily on that if feature extraction and selecting are effective. Complicated image such as medical image and remote sensing image, belong to image with natural textures, this kind of image is always of high resolution, with many layers of gray degree, and a very intricate shape structure. Because there are no obvious shapes, but only distributions of some gray degrees and colors in these images, so for them, there are no good methods yet for feature extraction and region recognition. In this paper, based on information augmentation and kinetics, we present a learning algorithm, which can be used to do region classification of the above-mentioned images with natural textures. We applied our algorithm to recognition of image with natural textures and obtained a good result.

关键词： pattern recognition information entropy augmentation machine learning NN

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Landsat TM multi-spectral classification using support vector machine method in low-hill areas

Landsat TM multi-spectral classification using support vecto...

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conference on image processing and pattern recognition in remote sensing II

作者： Zhao, SH Ke, CQ Dong, XQ Li, JL Feng, XZ Peking Univ Inst Remote Sensing & GIS Beijing 100871 Peoples R China

ISBN: (纸本)0819456187

Support vector machine (SVM) is a newly learning machine. In the paper, it applied the SVM method to research on remote sensing multi-spectral classification using Landsat TM data. It selected the typical low-hill area as study site, which was located on the southern of the Yangze River, China. The land cover types were divided into six categories, which were the waterbody, the construction land, the paddy field, the woodland, the teagarden, and the bare land, etc. The classification of the study site using the Kohonen networks method was also given. The classification results show that classification accuracy of the SVM method is better than that of the Kohonen Networks method. Especially it could discriminate the woodlands from the mountainous shadow. In conclusion, the SVM method could gain higher classification accuracy using smaller training sample in low-hill area. It could also solve the confusion problems among the ground objects.

关键词： multi-spectral classification SVM Kohonen Networks low-hill area

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A band-weighted landuse classification method for multispectral images

A band-weighted landuse classification method for multispect...

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conference on Computer Vision and pattern recognition

作者： Pan, CH Wu, G Prinet, V Yang, Q Ma, SD Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)0769523722

Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper we propose a hybrid method fusing edges and regions information for the landuse classification of multispectral images. It mainly includes the steps of image pre-processing, initial segmentation and region merging. Especially, a novel spatial mean shift procedure is proposed so that some information can be extracted and used in the successive steps. Aiming at the multispectral images processing, we also design a band weighting strategy that give a proper weight to each band adaptively according to the region to be processed. Experimental results on the Landsat TM and ETM+ images validate the performance of the proposed method.

关键词： pattern recognition

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Investigations into the analysis of remote sensing images with a growing neural gas pattern recognition algorithm

Investigations into the analysis of remote sensing images wi...

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IEEE International Joint conference on Neural Networks (IJCNN 2005)

作者： Lalonde, K S Dakota Sch Mines & Technol Inst Atmospher Sci Rapid City SD 57701 USA

ISBN: (纸本)0780390482

The Growing Neural Gas (GNG) pattern recognition algorithm is an unsupervised algorithm which inserts nodes into the state space of the training data. Observations of the behavior of the algorithm lead to the hypothesis that this method may be an efficient pre-classification clustering algorithm for data in highly discrete state spaces, as in satellite remote sensing images. The GNG algorithm was used to train a network using a Landsat image from Wyoming. The initial results of this investigation were extremely positive. The image derived from the trained GNG network is difficult to distinguish from the source image. Preliminary statistical results also indicate a high degree of correlation between the source and resultant images.

关键词： image analysis

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Clustering multispectral images: a tutorial

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2005年第1-2期77卷 3-17页

作者： Tran, TN Wehrens, R Buydens, LMC Univ Nijmegen Analyt Chem Lab NSRIM Res Sch NL-6525 ED Nijmegen Netherlands

A huge number of clustering methods have been applied to many different kinds of data set including multivariate images, such as magnetic resonance images (MRI) and remote sensing images. However, not many methods include spatial information of the image data. In this tutorial, the major types of clustering techniques are summarized. Particular attention will be devoted to the extension of clustering techniques to take into account both spectral and spatial information of the multivariate image data. General guidelines for the optimal use of these algorithms are given. The application of pre- and post-processing methods is also discussed. (c) 2004 Elsevier B.V All rights reserved.

关键词： pattern recognition unsupervised classification

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The building recognition of high resolution satellite remote sensing image based on wavelet analysis

The building recognition of high resolution satellite remote...

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International conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Qin, Qi-Ming Chen, Si-Jin Wang, Wen-Jun Chen, De-Zhi Wang, Lin Institute of Remote Sensing and GIS Peking University Beijing 100871 China Department of Communication Engineering Yanshan University Qinhuangdao Hebei 066004 China

ISBN: (纸本)078039092X

This paper imported wavelet analysis and wavelet descriptor into the building recognition of high resolution satellite remote sensing image, and brought forward the building recognition method based on wavelet descriptor in theory: firstly used wavelet analysis to do image segmentation in order to wipe off the large blocks of vegetation area in the image, then used dyadic wavelet to extract the edge, and used coordinate wavelet descriptor to describe the detected building contour, after that, computed the affine invariants to recognize the buildings, at last employed shadow validating to ascertain if the detected edge was the real building edge. This paper also constructed the building recognition pattern database, and used a QuickBird RS image of Peking University to do the experiment and the experimental result proved the feasibility of the method. ©2005 IEEE.

关键词： pattern recognition

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Semantic discriminant mapping for classification and browsing of remote sensing textures and objects

Semantic discriminant mapping for classification and browsin...

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IEEE International conference on image processing (ICIP 2005)

作者： Fauqueur, J Kingsbury, N Anderson, R Univ Cambridge Signal Proc Grp Dept Engn Cambridge England

ISBN: (纸本)0780391349

We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks.

关键词： Conformal mapping

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