Image classification is one of the most important tasks of remote sensing information processing used for object recognition. In this paper, a novel scheme is proposed to improve the accuracy of hyperspectral image classification by amalgamating multiple feature vector sets and ensemble methods with different classifiers. Extracting the texture, color and object features of the satellite images...

Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer questions about the object, its attribution and its authenticity. However, these layers typically overlap and are oftentimes difficult to differentiate with the un...

A Graphical User Interface (GUI) was developed for a user-friendly implementation of a water depth correction model. The Interactive Data Language (IDL)-based tool provides the prospective users with an interface that can be applied to perform water depth correction on hyperspectral images that contain shallow water bodies containing benthic habitat information. Users can select a pixel or a su...

In this paper, Xunke County was studied using HJ-1A hyperspectral data for monitoring vegetation restoration after forest fires. The pre-processing procedure including data format conversion, image mosaicing and atmospheric correction. Support vector machine classification was used to perform surface feature identification based on the extracted spectral end-members. On that basis, the image ar...

The focus of this project was to develop and implement detection algorithms for imaging and non-imaging spectroscopy sensing modalities to best detect and identify explosive-related threats. The proposed approach included modeling of spectral variability, signal and image enhancement, and feature extraction for target detection, and computational implementation using GPUs. The developed algorit...

Hyperspectral imaging is a new technique in remote sensing which generates hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Each pixel collected by a hyperspectral remote sensing instrument is in fact a spectral signature of the underlying materials. Many algorithms attempt to find pure spectral signatures in the image data, called endmembers,...

BACKGROUND Segmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefor...

The Norwegian defence research establishment (FFI) is building a technology demonstrator for hyperspectral target detection. The demonstrator system will integrate hyperspectral and conventional imagers with on-board real-time processing for target detection. The system is built around a hyperspectral camera working mainly in the visible and near-infrared (VNIR) spectral range. Image data will ...

Since hyperspectral imagery (HSI) (or remotely sensed data) provides more information (or additional bands) than traditional gray level and color images, it can be used to improve the performance of image classification applications. A hyperspectral image presents spectral features (also called spectral signature) of regions in the image as well as spatial features. Feature reduction, selection...

This work deals with hyperspectral image analysis in the absence of ground-truth. The method adopts a projection pursuit (PP) procedure with entropy index to reduce the dimensionality followed by Markov Random Field (MRF) model based segmentation. Ordinal optimization approach to PP determines a set of “ good enough projections” with high probability the best among which is chosen with the help...