Hyperspectral imaging is a new technique in remote sensing that collects hundreds of images, at different wavelength values, for the same area in the surface of the Earth. For instance, the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) instrument operated by NASAs Jet Propulsion Laboratory collects 224 spectral channels in the wavelength range from 40 to 250 nanometers using narrow s...

This paper reviews the recently developed processing techniques for remotely sensed imagery, including very high resolution (VHR) information extraction, super resolution techniques, hyperspectral image processing and object detection, and also some artificial intelligence approaches.

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a singl...

We test the use of hyperspectral sensors for the early detection of the invasive denseflowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correctio...

Understanding the mineralogy and geochemistry of subsurface is key when assessing exploring for mineral deposits. To achieve this goal, rapid acquisition accurate interpretation drill core data are essential. Hyperspectral shortwave infrared imaging a non-destructive analytical method widely used in minerals industry to map with diagnostic features samples. In paper, we present an automated int...

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized ...