Multispectral Information Research Articles

Prediction of total cerebral tissue volumes in normal appearing brain from sub-sampled segmentation volumes.

The need for anatomical coverage and multi-spectral information must be balanced against examination and processing time to ensure high-quality, feasible imaging protocols for clinical research of cerebral development in normal-appearing brains. The focus of this study was to create and assess models to estimate total cerebral volumes of gray matter, white matter, and cerebrospinal fluid (CSF) from anatomically defined sub-samples of full clinical examinations. Pediatric patients (18F, 11M; aged 1.7 to 18.7, median 5.2 years) underwent a clinical imaging protocol consisting of 3 mm contiguous T1-, T2-, PD-, and FLAIR-weighted images after obtaining informed consent. Magnetic resonance imaging (MRI) sets were registered, RF-corrected, and then analyzed with a hybrid neural network segmentation and classification algorithm to identify normal brain parenchyma. The correlation between the image subsets and the total cerebral volumes of gray matter, white matter and CSF were examined through linear regression analyses. Five sub-sampled sets were defined and assessed in each patient to produce estimation models which were all significantly correlated (p < 0.001) with the total cerebral volumes of gray matter, white matter, and CSF. Volumes were estimated from as little as a single representative slice requiring minimal processing time, 27 min, but with an average estimation error of approximately 6%. Larger sub-samples of approximately three-quarters of the full cerebral volume required much more processing time, 2 h and 4 min, but produced estimates with an average error less than 2%. This study demonstrated that investigators can choose the amount of cerebrum sampled to optimize the acquisition and processing time against the degree of accuracy needed in the total cerebral volume estimates.

Remote sensing as a tool for bathymetric mapping of coral reefs in the Red Sea (Hurghada – Egypt)

For monitoring coral reefs, a bathymetric map is useful as a base map. Different methods have already been developed to map bathymetry using remote sensing. Two main groups can be distinguished. One group is using active remote sensing data; the other is based on passive sensor generated multi-spectral information. In this article, the focus is on the passive data method. A modified «depth of penetration» mapping method was implemented on a Landsat7 ETM+-image over Hurghada (Egypt). Some 420 depth measurements were used for ground-truthing and accuracy testing. The accuracy test revealed that the resulting bathymetric map is useful for coral reef mapping, but care should be taken when using it. Deviations from reality were caused by assumptions inherent to the theory used, field sampling, satellite image characteristics and errors during implementation of the method.

Estimation and validation of land surface broadband albedos and leaf area index from eo-1 ali data

The Advanced Land Imager (ALI) is a multispectral sensor onboard the National Aeronautics and Space Administration Earth Observing 1 (EO-1) satellite. It has similar spatial resolution to Landsat-7 Enhanced Thematic Mapper Plus (ETM+), with three additional spectral bands. We developed new algorithms for estimating both land surface broadband albedo and leaf area index (LAI) from ALI data. A recently developed atmospheric correction algorithm for ETM+ imagery was extended to retrieve surface spectral reflectance from ALI top-of-atmosphere observations. A feature common to these algorithms is the use of new multispectral information from ALI. The additional blue band of ALI is very useful in our atmospheric correction algorithm, and two additional ALI near-infrared bands are valuable for estimating both broadband albedo and LAI. Ground measurements at Beltsville, MD, and Coleambally, Australia, were used to validate the products generated by these algorithms.

Euclidian distance-weighted smoothing for quantitative MRI: application to intervoxel anisotropy index mapping with DTI

During the computation of intervoxel anisotropy features, the inclusion of both eigenvalues and eigenvectors reduces the effect of noise, but spatial averaging blurs the resulting maps. We propose a new adaptive technique that uses data-dependent weights in the averaging process so that the influence of each neighbor in the local window is proportional to the similarity of characteristics of the neighbor considered to those of the reference central voxel. This likeness criterion is based on the multidimensional Euclidian distance using the entire available multispectral information contained in the diffusion-weighted images. This solution is controlled by a single parameter β that results from a compromise between edge-preserving and noise-smoothing abilities. This Euclidian distance-weighted technique is a generic solution for filtering noise during parametric reconstruction. It was applied to map anisotropy using an intervoxel lattice index (LI) from experimental images of mouse brain in vivo and achieves noise reduction without distorting small anatomical structures. We also show how to employ in the discrimination scheme the images not used in the estimation of the considered feature.

Comparison Of Standard And Image-filter Fusion Techniques

Image fusion in a general sense, can be defined as:’<the combination of two or more dljlerent images to form a new image by using a certain algorithm” [1]. It aims to extract flom a set of different images of the same area those properties which are unavailable by the single sensor and which form in a combined way a new image with a higher multispectral resolution. In standard image fusion one multispectral image band is entirely substituted by the panchromatic band. It is not new, however, that due to the incoherence of sensor performances the original multispectral information becomes distorted and hence, the data provides only limited use in the spectral classification of textural image information. In comparison, newly evolved image filter data fusion techniques preserve both spatial and multispectral information. Results of both techniques are shown here using a higher resolution panchromatic image (IRS-1 C) and a lower resolution multispectral image (SPOT XS). In detail, this paper discusses on one side the performance of three standard image fhsion techniques, Intensity Hue Saturation (IHS), Principle Component Analysis (PCA) and High Pass Filter (HPF) and on the other side the use of a newly evolved image fusion technique, the Adaptive Image Filter (AIF) fksion. Results are visually assessed for image interpretability and statistically for spectral classification of textural data. From this assessment the IHS and PCA fusion achieved similar good results for visual image interpretation. If fused images are used for subsequent image classification, then the AIF fhsion is the preferable one, preserving the multispectral information while adding textural and structural detail. © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved. Web: www.witpress.com Email witpress@witpress.com Paper from: Data Mining III, A Zanasi, CA Brebbia, NFF Ebecken & P Melli (Editors). ISBN 1-85312-925-9

Invariant algebra and the fusion of multi-spectral information

The astonishingly large varieties of multi-sensor scene signatures that need to be considered for building a robust machine-based scene analysis system require that one explore the possibility of deriving multi-sensor representations that are at the least invariant to scale, translation, and rotation with respect to the observer. The work presented in this paper is a probability density function (PDF)-based technique that uses the theory of invariant algebra to derive algebraic expressions that remain constant under an object's joint geometrical and surface material changes. This generalization of the geometrical invariants to cover both geometrical as well as physical changes in the images of a scene is a significant contribution to the state-of-the-art. Two cases of similar and dissimilar sensor types are considered. The fused invariants for the case of similar sensor types are unchanged under material as well as under affine transformations. For the case of dissimilar sensors, the approach leads to the derivation of the multi-sensor algebraic expressions that remain unchanged under scale, translation and two-dimensional rotation with respect to the observer. An analysis of the computational complexities of the presented techniques and its comparison with a typical non-invariant approach illustrate the noticeable advantages of the invariant method.

Integrated method for boundary delineation of agricultural fields in multispectral satellite images

Most agricultural statistics are calculated per field, and it is well known that classification procedures for homogeneous objects produce better results than per-pixel classification. In this study, a multispectral segmentation method for automated delineation of agricultural field boundaries in remotely sensed images is presented. Edge information from a gradient edge detector is integrated with a segmentation algorithm. The multispectral edge detector uses all available multispectral information by adding the magnitudes and directions of edges derived from edge detection in single bands. The addition is weighted by edge direction, to remove noise and to enhance the major direction. The resulting edge from the edge detection algorithm is combined with a segmentation method based on a simple ISODATA algorithm, where the initial centroids are decided by the distances to the edges from the edge detection step. From this procedure, the number of regions will most likely exceed the actual number of fields in the image and merging of regions is performed. By calculating the mean and covariance matrix for pixels of neighboring regions, regions with a high generalized likelihood-ratio test quantity will be merged. In this way, information from several spectral bands (and/or different dates) can be used for delineating field borders with different characteristics. The introduction of the ISODATA classifier compared with a previously used region growing procedure improves the output. Some results are compared with manually extracted field boundaries.

Wavelet Compression and the Automatic Classification of Urban Environments Using High Resolution Multispectral Imagery and Laser Scanning Data

The field of wavelets has opened up new opportunities for the compression of satellite data. This paper examines the influence of data compression on the automatic classification of urban environments. Data from Daedalus airborne scanner imagery is used. Laser scanning altitude data is introduced as an additional channel alongside the spectral channels thus effectively integrating the local height and multispectral information sources. In order to incorporate context information, the feature base is expanded to include both spectral and non-spectral features. A maximum likelihood classification is then applied. It is demonstrated that the classification of urban scenes is considerably improved by fusing multispectral and geometric data sets. The fused imagery is then systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using a wavelet-based compression algorithm. The compressed imagery is then classified using the approach described hereabove. Analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be employed without adversely affecting the segmentation results.

Computer-enhanced multispectral remote sensing data: a useful tool for the geological mapping of Archean terrains in (semi)arid environments

Computer-based remote sensing techniques offer a unique chance to analyze and map the surface geology of remote arid environments in a relatively short time and at low costs. On the basis of multispectral Landsat TM 5, panchromatic SPOT and aerial image scanning data, the lithological and structural characteristics of a part of the Archean Pilbara granite-greenstone terrain (3.5–2.8 Ga) in Western Australia have been analyzed by using complex mathematical image enhancement techniques. The application of a sophisticated image processing software on an adequate PC (Dual Pentium II, 300 MHz) was necessary for the file management of the datasets, the calculation of TM data ratios ( R), principal components (PC) and selected color composites (CC) which have been found more appropriate for the geological interpretation at a regional scale. These low resolution, but multispectral data (e.g. color composites) have been merged with high resolution panchromatic images to obtain new, high resolution images without loss of multispectral information. The interpretation of the new data contribute to a more detailed geological mapping of selected test sites which was studied during a field campaign in 1997.

168 MULTI-SENSOR DATA FUSION AND WAVELET COMPRESSION IN THE AUTOMATIC CLASSIFICATION OF URBAN ENVIRONMENTS

This paper examines the influence of wavelet compression on the automatic classification of urban environments. Airborne laser scanning data is introduced as an additional channel alongside the spectral channels of a color infrared image. This effectively integrates the local height and multi-spectral information sources. To incorporate context information the feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the classification of urban scenes is considerably improved by fusing multi-spectral and geometric data. The fused imagery is then systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using a wavelet-based compression algorithm. The compressed imagery is then classified using the approach described here-above. Analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be employed without adversely affecting the segmentation results.

A multichannel continuously selectable multifrequency electrical impedance spectroscopy measurement system.

There is increasing evidence that alterations in the electrical property spectrum of tissues below 10 MHz is diagnostic for tissue pathology and/or pathophysiology. Yet, the complexity associated with constructing a high-fidelity multichannel, multifrequency data acquisition instrument has limited widespread development of spectroscopic electrical impedance imaging concepts. To contribute to the relatively sparse experience with multichannel spectroscopy systems this paper reports on the design, realization and evaluation of a prototype 32-channel instrument. The salient features of the system include a continuously selectable driving frequency up to 1 MHz, either voltage or current source modes of operation and simultaneous measurement of both voltage and current on each channel in either of these driving configurations. Comparisons of performance with recently reported fixed-frequency systems is favorable. Volts dc (VDC) signal-to-noise ratios of 75-80 dB are achieved and the noise floor for ac signals is near 100 dB below the signal strength of interest at 10 kHz and 60 dB down at 1 MHz. The added benefit of being able to record multispectral information on source and sense signal amplitudes and phases has also been realized. Phase-sensitive detection schemes and multiperiod undersampling techniques have been deployed to ensure measurement fidelity over the full bandwidth of system operation.

Photo Ecometrics for Forest Inventory

In this paper, we report the results obtained from the application of digital photogrammetry and hyperspectral data analysis for forest inventory purposes. Our long term goal is to provide low-cost yet accurate estimates of as many important forest biophysical parameters as can be measured and inferred with airborne digital cameras. Accuracies of traditional multispectral image analysis algorithms of remotely sensed data are low. Traditional photo interpretation is error prone and expensive. We propose new image analysis strategies that make use of the 3D spatial morphological information from stereo images and the multispectral, texture and contextual information inherent in the imagery. Research on the use of 3D crown shape information in automated tree species recognition has not been reported before. The minimum requirements of image spatial resolution for deriving estimates of tree heights and crown size with high accuracies are not known. With digital photogrammetry, it has been proven that digital camera images can be georeferenced and orthorectified to an accuracy of one to several meters allowing for selecting ground control points directly from digital camera images for georectification of other images including high resolution satellite images. With the georeferenced and orthorectified digital camera images and the above parameters accurately determined, we can detect changes of species composition, height, crown closure, and diameter. These same techniques will significantly improve our ability to economically assess the accuracy of thematic vegetation maps.

Radiance spectra classification from the Ocean Color and Temperature Scanner on ADEOS

Multispectral information from the ocean color sensors of remote sensing satellites can be used to classify the ocean surface waters into a number of classes. Nine areas distributed over the Pacific Ocean have been used to demonstrate this approach. Unsupervised neural networks were used to separate water pixels from land and cloud pixels and classify water into a variety of ocean colors. Self-organizing feature maps chose radiance spectra by minimizing least square differences amongst multichannel pixels. Pixels with similar radiance spectra were coded with similar colors. It has been shown that radiance spectra, after correction for the atmospheric absorption of a standard atmosphere for varying Sun and satellite viewing angles, could be classified into a single set of radiance spectra that apply over the whole ocean. No ground truth data were required to make this classification. Examinations of the classified images showed that the method could extract a large number of ocean color categories and provide a basis to separate case 1 waters from the case 2 and ocean radiances with a high influence of the atmosphere. Also, areas of high pigment, inappropriately masked out by the conventional routine, were correctly classified. This opens the possibility that in the future a robust global algorithm for chlorophyll estimation might be constructed.

A new merging method and its spectral and spatial effects

A new SVR (Synthetic Variable Ratio) merging method is described which reproduces the spectral characteristics of an original multispectral image and spatial information of a panchromatic image with higher resolution very well. Different TM bands and SPOT pan images in two different large urban areas were used as test data to assess the new SVR method. The spectral and spatial effects of the new SVR method were assessed by using visual and graphical methods and the results were compared with those of the IHS (Intensity, Hue, Saturation) and PCS (Principal Component Substitution) methods. The results show that the spectral effect of the SVR method is the best of the three merging methods. The spatial effect of the SVR method is the same as that of the other two methods.

Investigations on synergy and complementarity of multi-spectral and anisotropy information from MOMS-02/D2-Mode 3-data for land use classification in the Sinaloa district of Mexico

Design and registration geometry of the MOMS-02 sensor allows for the first time to retrieve information about the surface property of anisotropic backscattering. Complementarity and synergism of multi-spectral and anisotropy classification approaches were investigated by analysing and comparing evaluations of MOMS-02/D2 mode 3 multi-spectral and stereo band data from the Sinaloa district of Mexico. Visual as well as computer based analysis indicates that in both approaches the stratification is determined by different biophysical parameters. The relation of signal response to the specific parameters of the surfaces are discussed, reflecting on fundamental error sources in estimating the anisotropy of surfaces as well as on technical limits due to the experimental status of the MOMS-02 sensor.

Advantages of list-mode acquisition of dynamic cardiac data [ECG-gated nuclear medicine imaging

Conventional frame-mode acquisition of multigated blood-pool (MUGA) studies is an imprecise means to study cardiac dynamics in part because the energy and ECG waveform information is not retained Early methods for list-mode acquisition were also somewhat limited in that only the (x,y) coordinate, a timing pulse and an R-wave detect pulse were stored. The authors have extended list-mode capability to include full storage of the ECG waveform and the energy value associated with each spatial coordinate. The spatial coordinates (x and y), energy value (E), and an encoded physiologic/timing signal (p/t) are stored on an event-by-event basis as 8-bit values. These four bytes are streamed onto a hard disk storage device allowing even stress gated blood pool studies to be recorded. List-mode acquisition gives greater flexibility than frame-mode. Among the demonstrable advantages this flexibility provides are the ability to: (1) Measure and correct respirational displacement of the ventricle to minimize motion blurring; (2) Form gated sequences representing only a specific aberrancy for detection of ectopic ventricular foci; and (3) Use the stored multispectral energy information to perform scatter correction on the dynamic images. To conclusively demonstrate the improved resolution of the list-mode methodology, a dynamic phantom was used to simulate blurring due to respiration. The advantages of list-mode warrant its further use, and it may be especially valuable for wall thickness measurements.

Detection of changes in remotely-sensed images by the selective use of multi-spectral information

In this Letter, an unsupervised algorithm for detecting changes in multi-spectral and multi-temporal remotely-sensed images is presented. Such an algorithm makes it possible to reduce the effects of 'registration noise' on the accuracy of change detection. In addition, it can be used to reduce the typologies of detected changes in order to better locate the changes under investigation.

Neural classification of SPOT imagery through integration of intensity and fractal information

It is well known that higher dimensional information essentially leads to better accuracy in remotely sensed image classification. This paper is aimed at land cover classification from SPOT-HRV imagery by the integration of multispectral intensity and texture information. In particular, fractal dimensions are extracted using a wavelet transform as image texture. A neural network approach to classification is adopted in this paper. The underlying network is a modified multilayer perceptron trained by a Kalman filtering technique. The main advantages of this network are (1) its non-backpropagation fashion of learning which leads to a fast convergence, (2) a built-in optimization function, and (3) global scale. Saving computer storage space and a fast learning capability are in particular suitable features for remote sensing applications. Correlation analysis was subsequently performed on both the intensity and fractal images. It was found that fractal information significantly improves the discrimination capability of heterogeneous area such as in urban regions, while it slightly degrades accuracy for homogeneous areas, such as open water. The overall classification performance is superior to results obtained using reflectance only. Improvements over heterogeneous areas are demonstrated.

Monitoring of NWP models by use of satellite data

AbstractThis paper describes some cases where satellite information contributes to beneficial monitoring of NWP model output. The real‐time observations and NWP parameters required for the methods used are described. Selected examples, including successful and less successful NWP developments, are used to illustrate the applicability of the methods. Key elements are the localisation and isentropic advection of potential vorticity (PV). Positive upper and middle tropospheric PV anomalies may in several weather situations be detected in the Meteosat WV imagery as dark areas in the rear of developing cyclones. This study indicates that we may have confidence in the model description of weather development when the NWP model output showing areas with high‐level potential vorticity corresponds in time and space with dry areas inferred from the WV images. However, when such correspondence is only poor, the confidence is shown to be lower. In such cases there is a need to adjust the NWP model output. Also large‐scale isentropic advection fields and cross‐sections from the NWP models are used together with the multi‐spectral AVHRR information from the NOAA polar‐orbiters. Mesoscale events related to deep convection and small‐scale wind fields in connection with polar low developments are investigated.

Classification of cloud complexity in severe weather conditions over the Ionian sea and the south-east Dodecanese using Meteosat data

Abstract A case study is presented in order to illustrate the usefulness of multi-spectral information for the classification of cloud complexity in severe weather conditions. Severe weather over Rodos island and the Ionian Sea on 20 October 1994 occurred just one day before the heavy rainfall over mainland Hellas, which due to sudden flooding over certain areas of Attiki, Hellas, led to the deaths of 10 people.