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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery II"

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Realtime online unsupervised detection and classification for remotely sensed imagery

Realtime online unsupervised detection and classification fo...

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

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

ISBN: (纸本)081945348X

Realtime online processing is important to provide immediate data analysis for resolving critical situations in real applications of hyperspectral imaging. We have developed a Constrained Linear Discriminant Analysis (CLDA) algorithm, an excellent approach to hyperspectral image classification, and investigated its realtime online implementation. Because the required prior object spectral signatures may be unavailable in practice, we propose its unsupervised version in this paper. The new algorithm includes unsupervised signature estimation in realtime followed by realtime CLDA algorithm for classification. The unsupervised signature estimation is based on linear mixture model and least squares error criterion. The preliminary result using an HYDICE scene demonstrates its feasibility and effectiveness.

关键词： realtime online processing unsupervised classification signature estimation CLDA algorithm Remotely Sensed Image hyperpectral imagery

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Determining noise in hyperspectral imagery for the application of oversampling to supervised classification

Determining noise in hyperspectral imagery for the applicati...

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

作者： Hunt, S Laracuente, J Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00681 USA

ISBN: (纸本)081945348X

The spectrum of most objects in a hyperspectral image is oversampled in the spectral dimension due to the images having many closely spaced spectral samples. This oversampling implies that there is redundant information in the image which can be exploited to reduce the noise, and so increase the correct classification percentage. Oversampling techniques have been shown to be useful in the classification of hyperspectral imagery. These previous techniques consist of a lowpass filter in the spectral dimension whose characteristics are chosen based on the average spectral density of many objects to be classified. A better way of selecting the characteristics of the filter is to calculate the spectral density and oversampling of each object, and use that to determine the filter. The algorithm proposed here exploits the fact that the system is supervised to determine the oversampling rate, using the training samples for this purpose. The oversampling rate is used to determine the cutoff frequency for each class, and the highest of these is used to filter the whole image. Two pass approaches, where each class in the image is filtered with its own filter, were studied, but the increase in performance did not justify the increase in computational load. The results of applying these techniques to data to be classified are presented. The results, using AVIRIS imagery, show a significant improvement in classification performance.

关键词： hyperspectral oversampling classification

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Assessment of effects of lossy compression of hyperspectral image data

Assessment of effects of lossy compression of hyperspectral ...

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

作者： Su, JK Hsu, SM Orloff, S MIT Lincoln Lab Lexington MA 02420 USA

ISBN: (纸本)081945348X

hyperspectral imaging (HSI) sensors provide imagery with hundreds of spectral bands, typically covering VNIR and/or SWIR wavelengths. This high spectral resolution aids applications such as terrain classification and material identification, but it can also produce imagery that occupies well over 100 MB, which creates problems for storage and transmission. This paper investigates the effects of lossy compression on a representative HSI cube, with background classification serving as an example application. The compression scheme first performs principal components analysis spectrally, then discards many of the lower-importance principal-component (PC) images, and then applies JPEG2000 spatial compression to each of the individual retained PC images. The assessment of compression effects considers both general-purpose distortion measures, such as root mean square difference, and statistical tests for deciding whether compression causes significant degradations in classification. Experimental results demonstrate the effectiveness of proper PC-image rate allocation, which enabled compression at ratios of 100-340 without producing significant classification differences. Results also indicate that distortion might serve as a predictor of compression-induced changes in application performance.

关键词： hyperspectral imagery lossy compression background classification

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A general spectral target signature transform

A general spectral target signature transform

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

作者： Mayer, R Bucholtz, F Scribner, D Kruer, M SFA Largo MD 20774 USA

ISBN: (纸本)081945348X

This report examines a series of multispectral or hyperspectral image data cubes collected of a given scene at different times. The diagonal, whitening/dewhitening, and target CV (Covariance) transformations use collected image data of spatially overlapping regions from data sets collected at different times to evolve target spectral signatures. The previously studied registration-free transformations are described using a single general equation and form a subset of a larger family of accurate transforms. The diagonal, whitening/dewhitening, and target CV transformations are characterized by a parameter n having n=0,1;2 respectively. The transformed target signatures, used in matched filter searches, are tested on images taken from two data collects that use different sensors, targets, and backgrounds. Transforms with n between 0 and 2 yield the largest Target to Clutter Ratio (TCR) and remain relatively constant for 0

关键词： target detection target identification matched filters multispectral imagery hyperspectral imagery

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Spectral quality metrics for VNIR and SWIR hyperspectral imagery

Spectral quality metrics for VNIR and SWIR hyperspectral ima...

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

作者： Kerekes, JP Hsu, SM MIT Lincoln Lab Sensor Technol & Syst Applicat Grp Cambridge MA 02139 USA

ISBN: (纸本)081945348X

Current image quality approaches are designed to assess the utility of single band images by trained image analysts. While analysts today are certainly involved in the exploitation of spectral imagery, automated tools are generally used as aids in the analysis and offer hope in the future of significantly reducing the timeline and analysis load. Thus, there is a recognized need for spectral image quality metrics that include the effects of automated algorithms. We have begun initial efforts in this area through the use of a parametric modeling tool to gain insight into parameter dependence on system performance in unresolved object detection applications. An initial Spectral Quality Equation (SQE) has been modeled after the National imagery Interpretation Rating Scale General Image Quality Equation (NiiRS GIQE). The parameter sensitivities revealed through the model-based trade studies were assessed through comparison to analogous studies conducted with available data. This current comparison has focused on detection applications using sensors operating in the VNIR and SWIR spectral regions. The SQE is shown with key image parameters and sample coefficients. Results derived from both model-based trade studies and empirical data analyses are compared. Extensions of the SQE approach to additional application areas such as material identification and terrain classification are also discussed.

关键词： spectral imaging spectral quality

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The effect of wavelet-based dimension reduction on neural network classification and subpixel targeting algorithms

The effect of wavelet-based dimension reduction on neural ne...

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

作者： Rand, RS Bosch, EH USA Engn Res & Dev Ctr Topograph Engn Ctr Alexandria VA 22315 USA

ISBN: (纸本)081945348X

The effect of using Adaptive Wavelets is investigated for dimension reduction and noise filtering of hyperspectral imagery that is to be subsequently exploited for classification or subpixel analysis. The method is investigated as a possible alternative to the Minimum Noise Fraction (MNF) transform as a preprocessing tool. Unlike the MNF method, the wavelet-transformed method does not require an estimate of the noise covariance matrix that can often be difficult to obtain for complex scenes (such as urban scenes). Another desirable characteristic of the proposed wavelet transformed data is that, unlike Principal Component Analysis (PCA) transformed data, it maintains the same spectral shapes as the original data (the spectra are simply smoothed). In the experiment, an adaptive wavelet image cube is generated using four orthogonal conditions and three vanishing moment conditions. The classification performance of a Derivative Distance Squared (DDS) classifier and a Multilayer Feedforward Network (MLFN) neural network classifier applied to the wavelet cubes is then observed. The performance of the Constrained Energy Minimization (CEM) matched-filter algorithm applied to this data us also observed. HYDICE 210-band imagery containing a moderate amount of noise is used for the analysis so that the noise-filtering properties of the transform can be emphasized. Trials are conducted on a challenging scene with significant locally varying statistics that contains a diverse range of terrain features. The proposed wavelet approach can be automated to require no input from the user.

关键词： Adaptive Wavelets neural networks supervised classification derivative difference hyperspectral imagery linear mixture analysis Constrained Energy Minimization endmember selection terrain categorization abundance estimation

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Automatic subpixel target detection for multispectral remotely sensed imagery

Automatic subpixel target detection for multispectral remote...

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conference on Chemical and Biological Standoff Detection ii

作者： Ren, H Natl Cent Univ Ctr Space & Remote Sensing Res Chungli 32054 Taiwan

ISBN: (纸本)0819455377

Least square unmixing approach has been successfully applied to hyperspectral remotely sensed images for subpixel target detection. It can detect target with size less than a pixel by estimating its abundance fraction resident in each pixel. In order for the this approach to be effective, the number of bands must be larger than or equal to that of signatures to be classified, i.e., the number of equations should be no less than the number of unknowns. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. It is known as band number constraint (BNC). Such inherent constraint is not an issue for hyperspectral images since they generally have hundreds of bands, which is more than the number of signatures resident within images. However, this may not be true for multispectral images where the number of signatures to be classified might be greater than the number of bands. In order to relax this constraint, an extension of the least square approach is presented. With a set of least square filters that are nonlinearly combined, endmember detection for multispectral images can be realized. Furthermore, to detect targets in unknown background is a greater challenge. That is the well known Automatic Target Recognition (ATR) programs. In this paper, we also proposed a multispectral Target Generation Process (MTGP) that will automatic search for potential targets in the image scene. The effectiveness of the proposed method is evaluated by SPOT images. The experimental results show significantly improves in classification performance than Orthogonal Subspace Projection (OSP) and Automatic Target Detection and Classification Algorithm (ATDCA).

关键词： hyperspectral multispectral least square band number constraint (BNC) automatic target recognition (ATR) multispectral target generation process (MTGP) orthogonal subspace projection (OSP) automatic target detection and classification algorithm (ATDCA)

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Matching image feature structures using shoulder analysis method

Matching image feature structures using shoulder analysis me...

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

作者： Kovalerchuk, B Sumner, W Curtiss, M Kovalerchuk, M Chase, R Cent Washington Univ Dept Comp Sci Ellensburg WA 98929 USA

ISBN: (纸本)081945348X

The problems of imagery registration, conflation, fusion and search require sophisticated and robust methods. An algebraic approach is a promising new option for developing such methods. It is based on algebraic analysis of features represented as polylines. The problem of choosing points when attempting to prepare a linear feature for comparison with other linear features is a significant challenge when orientation and scale is unknown. Previously we developed an invariant method known as Binary Structural Division (BSD). It is shown to be effective in comparing feature structure for specific cases. In cases where a bias of structure variability exists however, this method performs less well. A new method of Shoulder Analysis (SA) has been found which enhances point selection, and improves the BSD method. This paper describes the use of shoulder values, which compares the actual distance traveled along a feature to the linear distance from the start to finish of the segment. We show that shoulder values can be utilized within the BSD method, and lead to improved point selection in many cases. This improvement allows images of unknown scale and orientation to be correlated more effectively.

关键词： data fusion imagery conflation algebraic invariants geospatial feature polyline match measure of correctness structural similarity structural interpolation

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Determining the dimensionality of hyperspectral imagery for unsupervised band selection

Determining the dimensionality of hyperspectral imagery for ...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery IX

作者： Umaña-Díaz, A Vélez-Reyes, M Univ Puerto Rico Dept Elect & Comp Engn Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819449539

This paper addresses the problem of estimating the dimension of a hyperspectral image. Spanning and intrinsic dimension concepts are studied as ways to determine the number of degrees of freedom needed to represent a hyperspectral Image. algorithms for the estimation of spanning and intrinsic dimension are reviewed and applied to hyperspectral images. Estimators are evaluated and compared using simulated and AVIRIS data. The final objective of this work is to develop an algorithm to determine the number of bands to select in a band subset selection algorithm.

关键词： dimensionality reduction band selection data dimensionality

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Dimensionality reduction in hyperspectral imagery

Dimensionality reduction in hyperspectral imagery

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery IX

作者： Gillis, D Bowles, J Winter, ME USN Res Lab Washington DC 20375 USA

ISBN: (纸本)0819449539

In this paper we examine how the projection of hyperspectral data into smaller dimensional subspaces can effect the propagation of error. In particular, we show that the nonorthogonality of endmembers in the linear mixing model can cause small changes in band space (as, for example, from the addition of noise) to lead to relatively large changes in the estimated abundance coefficients. We also show that increasing the number of endmembers can actually lead to an increase in the amount of possible error.

关键词： hyperspectral analysis dimensionality analysis spectral demixing

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