Visualization and analysis of large three-dimensional hyperspectral images
نویسندگان
چکیده
New technologies for Secondary Ion Mass Spectrometry (SIMS) produce three-dimensional hyperspectral chemical images with high spatial resolution and fine mass-spectral precision. SIMS imaging of biological tissues and cells promises to provide an informational basis for important advances in a wide variety of applications, including cancer treatments. However, the volume and complexity of data pose significant challenges for interactive visualization and analysis. This paper describes new methods and tools for computer-based visualization and analysis of SIMS data, including a coding scheme for efficient storage and fast access, interactive interfaces for visualizing and operating on three-dimensional hyperspectral images, and spatio-spectral clustering and classification.
منابع مشابه
Hyperspectral Image Classification Based on the Fusion of the Features Generated by Sparse Representation Methods, Linear and Non-linear Transformations
The ability of recording the high resolution spectral signature of earth surface would be the most important feature of hyperspectral sensors. On the other hand, classification of hyperspectral imagery is known as one of the methods to extracting information from these remote sensing data sources. Despite the high potential of hyperspectral images in the information content point of view, there...
متن کاملICA-based fusion for color display of hyperspectral images
Hyperspectral images, contain data from a large number of contiguous bands and, therefore, cannot be displayed directly using a color display system. In this paper, an independent component analysis-based (ICA-based) approach for the problem of fusing hyperspectral images to three-band images for color display purposes is proposed. Correlation coefficient and mutual information (ICA-CCMI) are u...
متن کاملOverlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery
Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...
متن کاملEfficient encoding and rapid decoding for interactive visualization of large three-dimensional hyperspectral chemical images.
Interactive visualization of data from a new generation of chemical imaging systems requires coding that is efficient and accessible. New technologies for secondary ion mass spectrometry (SIMS) generate large three-dimensional, hyperspectral datasets with high spatial and spectral resolution. Interactive visualization is important for chemical analysis, but the raw dataset size exceeds the memo...
متن کاملSupervised Feature Extraction of Face Images for Improvement of Recognition Accuracy
Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images ar...
متن کامل