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16th International conference on pattern recognition, ICPR 2002

作者： Xu, Xiaoyin Miller, Eric L. Center for Subsurface Sensing and Imaging Systems Dept. of ECE Northeastern University BostonMA02115-5000 United States

ISBN: (纸本)0769516963

This paper presents a contrast stretch (CS) method based on minimum entropy constraint to enhance images obtained in remote sensing applications such as ground penetrating radar (GPR), synthetic aperture radar, and infra-red imagery. The CS enhances contrast of the low-contrast part of an image. In remote sensing, it is usually the desirable signals that are of low contrast while interference of high contrast. The CS modifies the original image such that pixel values above and below preset boundaries are set to zero and the maximum possible pixel value and the pixel values failing between the boundaries are stretched out to enhance the contrast of the image. Using the CS we can enhance the contrast of desirable signals from, for example, a buried landmine or an object obscured by some interference. On the other hand, the CS inevitably enhances other parts of a remote sensing images, such as clutters and measurement noise. Therefore there is a trade-off in using the CS. It is beneficial to find the correct "cut-off" boundaries in the CS in some optimal sense. We propose using minimum entropy as a criterion of looking for the optimal CS parameter. Using field data from GPR application, we show that improved image can be obtained which makes further processing such as detection more accurate. © 2002 IEEE.

关键词： Synthetic aperture radar

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Automated identification of endmembers from hyperspectral data using mathematical morphology

Automated identification of endmembers from hyperspectral da...

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conference on image and Signal processing for remote sensing VII

作者： Plaza, A Martínez, P Gualtieri, JA Pérez, RM Univ Extremadura Dept Informat GRNPS Badajoz Spain

ISBN: (纸本)0819442666

One of the most widely used approaches to analyze hyperspectral data is pixel unmixing, which relies on the identification of the purest spectra from the data cube. Once these elements, known as "endmembers", are extracted, several methods can be used to map their spatial distributions, associations and abundances. A large variety of methodologies have been recently proposed with the purpose of extracting endmembers from hyperspectral data. Nevertheless, most of them only rely on the spectral response;spatial information has not been fully exploited yet, specially in unsupervised classification. The integration of both spatial and spectral information is becoming more relevant as the sensors tend to increase their spatial/spectral resolution. Mathematical morphology is a non-linear image analysis and pattern recognition technique that has proved to be especially well suited to segment images with irregular and complex shapes, but has rarely been applied to the classification/segmentation of multivariate remote sensing data. In this paper we propose a completely automated method, based on mathematical morphology, which allows us to integrate spectral and spatial information in the analysis of hyperspectral images. The accuracy of the proposed algorithm is tested by its application to real hyperspectral data, and the results provided are compared to those found using other existing endmember extraction algorithms.

关键词： remote sensing hyperspectral data spatial/spectral analysis endmember extraction image processing

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Drift ice recognition using remote sensing data by neural networks 9

Drift ice recognition using remote sensing data by neural ne...

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9th International conference on Neural Information processing

作者： Nagao, T Mitsukura, Y Fukumi, M Akamatsu, N Univ Tokushima Fac Engn Dept Informat Sci & Intelligent Syst Tokushima 7708506 Japan

ISBN: (纸本)9810475241

In recent years, observation of a wide variety in the earth surface can be done by improvement of remote sensing technology. The purpose in this paper is to recognize a drift ice as thick. ice and thin ice, and sea using synthetic aperture rader (SAR) images. The recognition of the drift ice is achieved by using neural networks (NN). The neural network applies two, methods, a BP trained neural network and a Self-organizing map. Training data is image features extracted from SAR images. The number of methods of extracting the features are three, Fourier transform, high-order autocorrelation function (HACF), and image features based on a run length method. We carry out a comparative experiment, and demonstrate their effectiveness by means of computer simulation.

关键词： Ice

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Atmospheric correction of spectral imagery: Evaluation of the FLAASH algorithm with AVIRIS data 31

Atmospheric correction of spectral imagery: Evaluation of th...

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31st Applied imagery pattern recognition Workshop

作者： Matthew, MW Adler-Golden, SM Berk, A Felde, G Anderson, GP Gorodetzky, D Paswaters, S Shippert, M Spectral Sci Inc Burlington MA USA

ISBN: (纸本)076951863X

With its combination of good spatial and spectral resolution, visible to near infrared spectral imaging from aircraft or spacecraft is a highly valuable technology for remote sensing of the earth's surface. Typically it is desirable to eliminate atmospheric effects on the imagery, a process known as atmospheric correction. In this paper we review the basic methodology of first principles atmospheric correction and present results from the latest version of the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) algorithm. We show some comparisons of ground truth spectra with FLAASH-processed AVIRIS data, including results obtained using different processing options, and with results from the ACORN algorithm that derive from an older MODTRAN4 spectral database.

关键词： Aircraft High-resolution imaging image resolution Infrared imaging Infrared spectra Optical imaging remote sensing Space technology Space vehicles Spatial resolution

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Denoising and hyperbola recognition in GPR data

Denoising and hyperbola recognition in GPR data

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conference on image and Signal processing for remote sensing VII

作者： Belotti, V Dell'Acqua, F Gamba, P Univ Pavia Dipartimento Elettr I-27100 Pavia Italy

ISBN: (纸本)0819442666

The automatic analysis of Ground Penetrating Radar (GPR) images is an interesting topic in remote sensing image processing, since it involves the use of pre-processing, detection and classification tools with the aim of near-real time or at least very fast data interpretation. However, actual chains of preprocessing tools for GPR images do not consider usually denoising, essentially because most of the successive data interpretation is based on single radar trace analysis. So, no speckle noise analysis and denoising has been attempted, perhaps assuming that this point is immaterial for the following interpretation or detection tools. Instead, we expect that speckle denoising procedures would help. In this paper we address this problem, providing a detailed and exhaustive comparison of many of the statistical algorithms for speckle reduction provided in literature, i.e. Kuan, Lee, Median, Oddy and wavelet filters. For a more precise comparison, we use the Equivalent Number of Look (ENL), the Variance Ratio (VR). Moreover, we validate the denoising results by applying an interpretation step to the pre-processed data. We show that a wavelet denoising procedure results in a large improvement for both the ENL and VR. Moreover, it also allows the neural detector to individuate more targets and less false positive in the same GPR data set.

关键词： Ground Penetrating Radar denoising neural networks wavelet image processing

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Real-time VLSI architecture for hyperspectral image classification using the constrained linear discriminant algorithm

Real-time VLSI architecture for hyperspectral image classifi...

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IEEE International Geoscience and remote sensing Symposium (IGARSS 2002)/24th Canadian Symposium on remote sensing

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

ISBN: (纸本)078037536X

In this paper, a parallel/pipelined VLSI architecture designed to maximize concurrency and throughput is described for real-time hyperspectral image classification. To obtain a real-time architecture, we first simplified the constrained linear discriminant analysis (CLDA) algorithm and its computation flow. Next, we folded and modified its structure to reduce data dependency, to increase pipelining, and to minimize the silicon area. The required and yet slow data whitening process was avoided by using a modified target classifier, it is shown that applying the modified target classifier to the original data is equivalent to applying the original target classifier to the whitened data. Additionally, a high performance iterative matrix inversion algorithm simplifies the circuit complexity and improves processing time for repetitive matrix inversions and updates. Finally, to manage the high volume of data, an internal numerical representation is used. Additionally, a multiplexed bus reduces the I/O pins by sharing input data pins with pins to download registers with their initial values.

关键词： pattern recognition

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Assessment of sediment deposition rate in Bargi Reservoir using digital image processing

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2002年第sup1期47卷 S81-S92页

作者： Goel, MK Jain, SK Agarwal, PK Natl Inst Hydrol Roorkee 247667 Uttar Pradesh India

A reservoir is an integral component of a water resources system. Periodic evaluation of the sediment deposition pattern and assessment of available storage capacity of reservoirs is an important aspect of water resources management. The conventional techniques of quantification of sediment deposition in a reservoir, such as hydrographic surveys and the inflow-outflow methods, are cumbersome, costly and time consuming. Further, prediction of sediment deposition profiles using empirical and numerical methods requires a large amount of input data and the results are still not encouraging. Due to sedimentation, the water-spread area of a reservoir at various elevations keeps on decreasing. remote sensing, through its spatial, spectral and temporal attributes, provides synoptic and repetitive information on the water-spread area of a reservoir. By use of remote sensing data in conjunction with a geographic information system, the temporal change in water-spread area can be analysed to evaluate the sediment deposition pattern in a reservoir. A case study, related to the assessment of sediment deposition in Bargi Reservoir, Madhya Pradesh State, India, is presented. The reservoir was completed in 1988 and no hydrographic survey has yet been carried out. Under these circumstances, the sedimentation assessment using satellite data can guide the darn operators in updating the elevation-area-capacity table of the reservoir. The images for nine dates from the IRS-1C satellite, LISS-III sensor have been analysed using the ERDAS/IMAGINE software. The resulting sedimentation rate in the zone of study is about 229 m(3) km(-2) of catchment area per year.

关键词： reservoir sedimentation digital image processing water-spread area water identification Bargi Reservoir, Narmada, India

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Adaptive and smart interface for VCR remote Control using hand gestures

Adaptive and smart interface for VCR Remote Control using ha...

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16th International conference on pattern recognition (ICPR)

作者： Duric, Z Wechsler, H Yven, J George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

ISBN: (纸本)076951695X

This paper describes an adaptive and smart interface and shows its successful application for VCR remote "Control using hand gestures. The interface is capable of learning the user's operational habits and can offer self-help, aka wizard mode of operation;it can then monitor the user's gestures and maintain constant vigilance in an attempt to assist her through feedback using video display and/or loud-speaker. The availability of users' profiles is used in an adaptive fashion to enhance human-computer interactions and to make them intelligent, i.e., causal. The smart interface is suitable for handicapped users and it can be used for security purposes too.

关键词： remote control

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Entropy optimized contrast stretch to enhance remote sensing imagery

Entropy optimized contrast stretch to enhance remote sensing...

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作者： Xu, Xiaoyin Miller, Eric L. Center for Subsurface Sensing and Imaging Systems Dept. of ECE Northeastern University Boston MA 02115-5000 United States

This paper presents a contrast stretch (CS) method based on minimum entropy constraint to enhance images obtained in remote sensing applications such as ground penetrating radar (GPR), synthetic aperture radar, and infra-red imagery. The CS enhances contrast of the low-contrast part of an image. In remote sensing, it is usually the desirable signals that are of low contrast while interference of high contrast. The CS modifies the original image such that pixel values above and below preset boundaries are set to zero and the maximum possible pixel value and the pixel values falling between the boundaries are stretched out to enhance the contrast of the image. Using the CS we can enhance the contrast of desirable signals from, for example, a buried landmine or an object obscured by some interference. On the other hand, the CS inevitably enhances other parts of a remote sensing images, such as clutters and measurement noise. Therefore there is a trade-off in using the CS. It is beneficial to find the correct "cut-off" boundaries in the CS in some optimal sense. We propose using minimum entropy as a criterion of looking for the optimal CS parameter. Using field data from GPR application, we show that improved image can be obtained which makes further processing such as detection more accurate. © 2002 IEEE.

关键词： image enhancement

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Natural and artificial target recognition by hyperspectral remote sensing data

Natural and artificial target recognition by hyperspectral r...

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7th Battlespace Digitization and Network-Centric Warfare conference

作者： Zhang, B Liu, LY Zhao, YC Xu, GX Zheng, LF Tong, QX Chinese Acad Sci Inst Remote Sensing Applicat Beijing 100101 Chaoyang Dist Peoples R China

ISBN: (纸本)081944491X

Recent advances in remote sensing have led the way for the development of hyperspectral sensors and the applications of the hyperspectral data. Hyperspectral remote sensing is a relatively new technology, which is currently being investigated by researchers and scientists with regard to the detection and identification of minerals, terrestrial vegetation, and man-made materials and backgrounds. The airborne hyperspectral imaging data have operationally been used to a number of land-use, natural environment, geology, agriculture and other studies. In this study, airborne hyperspectral imaging data were tested in vegetation and man-made object identification. Natural grassland and artificial grassland, different types of crops, different types of forest and bush, different types of metal slabs in construction project have been precisely classified and greatly identified. In these works, the Operational Modular Imaging Spectrometer (ONUS) provides the imaging spectrometer data. OMIS has 128 spectral bands, including visible, short wave infrared, middle infrared and thermal infrared spectral region. Results suggest that hyperspectral imaging data, especially with short wave infrared and thermal infrared wavelength, have broad application perspectives in object identification.

关键词： hyperspectral remote sensing image processing target recognition

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