Search results for "multispectral"
showing 10 items of 242 documents
Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm
2017
Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is…
Prelaunch assessment of worldview-3 information content
2014
The upcoming WorldView-3 satellite is designed to collect unique data by combining very-high spatial resolution (VHR) with observation bands in the short wave infrared (SWIR) in addition to the visible and near-infrared (VNIR) multispectral and panchromatic bands currently available on the VHR WorldView-2 system. These SWIR bands were specifically selected in order to target unique reflectance and absorption features presented by various surface materials and should, therefore, significantly improve the platforms information content for many image mining applications. This presentation explores the information content available to the WorldView-3 platform in two ways. First, second-order st…
Structured Output SVM for Remote Sensing Image Classification
2011
Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…
Feature selection with Ant Colony Optimization and its applications for pattern recognition in space imagery
2016
This paper presents a feature selection (FS) algorithm using Ant Colony Optimization (ACO). It is inspired by the particular behavior of real ants, namely by the fact that they are capable of finding the shortest path between a food source and the nest. There are considered two ACO-FS model applications for pattern recognition in remote sensing imagery: ACO Band Selection (ACO-BS) and ACO Training Label Purification (ACO-TLP). The ACO-BS reduces dimensionality of an input multispectral image data by selecting the “best” subset of bands to accomplish the classification task. The ACO-TLP selects the most informative training samples from a given set of labeled vectors in order to optimize the…
Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods
2006
We describe some applications of linear and nonlinear pro- jection methods in order to reduce the number of spectral bands in Land- sat multispectral images. The nonlinear method is curvilinear component analysis CCA, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis PCA, a linear method. The principle of CCA consists in reproducing the topol- ogy of the original space projection points in a reduced subspace, keep- ing the maximum of information. Our conclusions are: CCA is an im- provement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA called CCAinitP…
Studies on the Effectiveness of Multispectral Images for Face Recognition: Comparative Studies and New Approaches
2013
In this paper, we investigate face recognition in unconstrained illumination conditions. A twofold contribution is proposed: First, three state of the art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are challenged against the IRIS-M3 multispectral face data base to evaluate their robustness against high illumination variation. Second, we propose to enhance the Performance of the three mentioned algorithms, which has been drastically decreased because of the non-monotonic illumination variation that distinguishes the IRIS-M3 face database. Instead of the usual braod band images…
A comparative study of best spectral bands selection systems for face recognition
2014
Multispectral images (MI) have shown promising capabilities to solve problems resulting from high illumination variation in face recognition. However, the use of MI, with the huge number of captured spectral bands for each subject, is impractical unless a system for best spectral bands selection (BSBS) is used. In this work, first we give an up to date overview of the existing BSBS techniques proposed for face recognition. We aim to highlight the imporatnce of this component of MI based systems. The reviewed techniques are then experimented using the multispectral face database IRIS - M3 to compare their performances. To the best of our knowledge this is the first study that reviews and com…
New Cloud Detection Algorithm for Multispectral and Hyperspectral Images: Application to ENVISAT/MERIS and PROBA/CHRIS Sensors
2006
This work presents a new methodology that faces the problem of accurate identification of location and abundance of clouds in multispectral images acquired by space-borne sensors working in the visible and near-infrared (VNIR) spectral range. The amount of images acquired over the globe every day by the instruments on board Earth Observation satellites makes inevitable that many of these images present cloud covers. The objective of this work is to develop and validate a method that takes advantage of the high spectral and radiometric resolution, and the specific band locations (e.g. the oxygen band) of present multispectral sensors to increase the cloud detection accuracy. Moreover, the me…
Towards single snapshot multispectral skin assessment
2012
Skin assessment technology based on comparative analysis of single-pixel RGB signal values at poly-chromatic illumination has been proposed. Multi-spectral imaging information from a single snapshot RGB image data set with the inter-channel crosstalk correction can be extracted this way. Proof-of-concept evaluations and measurement results are presented and discussed. Potential of bi-chromatic illumination for skin hemoglobin mapping during arterial occlusion test has been demonstrated.
MULTISPECTRAL VENOUS IMAGES ANALYSIS FOR OPTIMUM ILLUMINATION SELECTION
2013
International audience; Intravenous (IV) catheterization is the most important phase in medical practices of daily life. It is hard to localize veins in patients who have deep veins, minor age or dark skin; hence multiple attempts become indispensable for proper catheterization in such cases. Near Infrared (NIR) Imaging allow to visualize the veins underneath the skin of persons having non-visibility of veins problem. This paper reports the pre-selection of illuminants that ensure best veins/tissues contrast for patients having different skin tone. The sample subjects have been divided in four different classes based on the Luminance value of their skin tone in order to extract the best ill…