Hyperspectral sensors collect information as a set of images represented by different bands. Hyperspectral images are threedimensional images with sometimes over 100 bands where as regular images have only three bands: red, green and blue. Each pixel has a hyperspectral signature that represents different materials. Hyperspectral images can be used for geology, forestry and agriculture mapping,...

This paper reports the development of a spectral reconstruction technique for predicting hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for ...

abstract the amount of needed fertilizer in agricultural soils depends to the type of crop. absence of enough fertilizer may cause in tensions in plants and consequently result in decrease in amount of product. according to the agricultural literature, soils containing total nitrogen amounts (n %) between 0.1-0.15 percent are considered as soils containing normal amounts of nitrogen. the values...

Anomaly Detection (AD) has recently become an important application of target detection in hyperspectral images. The Reed-Xialoi (RX) is the most widely used AD algorithm that suffers from “small sample size” problem. The best solution for this problem is to use Dimensionality Reduction (DR) techniques as a pre-processing step for RX detector. Using this method not only improves the detection p...

This paper highlights SSC San Diego contributions to the research and development of hyperspectral technology. SSC San Diego developed the real-time, onboard hyperspectral data processor for automated cueing of high-resolution imagery as part of the Adaptive Spectral Reconnaissance Program (ASRP), which demonstrated a practical solution to broad area search by leveraging hyperspectral phenomeno...

Classification of heterogeneous classes present in the Hyperspectral image is one of the recent research issues in the field of remote sensing. This work presents a novel technique that classifies Hyperspectral images that contain number of classes by making use of the image moments. Recently, researchers have introduced a number of neural network models and structured output based methods for ...

Hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. However, the stochastic data analysis approaches that have been successfully applied to multispectral data are not as effective for hyperspectral data as well. Various investigations indicate that the key problem that causes poor performance in the stochastic approaches t...

In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit ...

Hyperspectral image compression has received considerable interest in recent years due to the enormous data volumes collected by imaging spectrometers for Earth Observation. JPEG2000 is an important technique for data compression, which has been successfully used in the context of hyperspectral image compression, either in lossless and lossy fashion. Due to the increasing spatial, spectral, and...