Hyperspectral imaging has become a very active field in both theoretical and application-related research in remote sensing since its emergence in the 1980s [1], [2]. The first airborne hyperspectral sensor, called Airborne Imaging Spectrometer (AIS), was designed in the early 1980s [3]; an advanced airborne sensor, i.e., Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), began to provide...

In this work, a novel methodology based on hyperspectral imagery with enhanced Darkfield microscopy for probing and characterizing changes in blood cell components was tested. Two main categories of blood cells were analyzed, red and white blood cells. Unique spectral signatures of ordinary and most common deformed morphologies of red blood cells were identified. Moreover, examination of white ...

The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on watershed, is composed of a spectral classification to obtain the markers and a vectorial gradient which gives the spatial information. Several alternative gradients are adapted to the different hyperspectral functions. ...

A hyperspectral imaging system demonstrated potential to detect surface fecal and ingesta contaminants on poultry carcasses. Hyperspectral imagery were processed and analyzed with four pre-processing methods considering calibration and spectral smoothing approaches. A band-ratio image processing algorithm, using 2-wavelengths (I565/I517) and 3-wavelengths (I576 I616)/ (I529 I616) equations, was...

This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration. TGMS is designed to be robust against...

This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of hyperspectral imagery. The moment invariants of an image can de...

Hyperspectral images have been used in anomaly and change detection applications such as search and rescue operations where it is critical to have fast detection. However, conventional Reed-Xiaoli (RX) algorithm [6] took about 600 seconds using a PC to finish the processing of an 800x1024 hyperspectral image with 10 bands. This is not acceptable for real-time applications. A more recent algorit...

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 ...

Hyperspectral sensors observe hundreds or thousands of narrow contiguous spectral bands. The use of hyperspectral imagery for remote sensing applications is new and promising, yet the characterization and analysis of such data by exploiting both spectral and spatial information have not been extensively investigated thus far. A generic methodology is presented for detecting and compensating ano...