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 ...

Machine learning algorithms have demonstrated impressive results for land cover mapping from hyperspectral data. To enhance generalization capabilities of statistical models, active (AL) methods guide the annotation training data set by querying most informative samples. The classifier can then be performed on an optimal set. We bring under same framework uncertainty, representativeness, and pe...

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...

Hyperspectral imaging offers new opportunities for inter-person facial discrimination. However, compact and discriminative feature extraction from high dimensional hyperspectral image cubes is a challenging task. We propose a spatio-spectral feature extraction method based on the 3D Discrete Cosine Transform (3D-DCT). The 3D-DCT optimally compacts information in the low frequency coefficients. ...

this work aims to extract the mineralogical constituents of the lahroud hyperion scene situated in the nw of iran. like the other push-broom sensors, hyperion images suffer from spectral distortions, namely the smile effect. the corresponding spectral curvature is defined as an across-track wavelength shift from the nominal central wavelength, and alters the pixel spectra. the common “column me...