The hope of coastal HyperSpectral Imaging (HSI) data is that it will provide the necessary data stream to simultaneously describe the atmospheric and water column optical properties. However, the success in achieving this goal is contingent upon the sensitivity and precision of the calibration of remote sensing instrumentation deployed. Building upon the progress achieved in the calibration of ...

In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters-chl...

With the development of sensor technology, the spectral resolution of remote sensing image is continuously improved. The appearance of the hyperspectral remote sensing is a tremendous leap in the field of remote sensing. The increasing availability of hyperspectral data and image has enriched us with better and finer data and it also enable us a much stronger ability to identify features. Howev...

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-base...

Oil-gas reservoir exploration using hyperspectral remote sensing, which based on the theory of hydrocarbon microseepage information and fine spectral response of target, is a new direction for the application of remote sensing technology. In this paper, Qaidam Basin and Liaodong Bay in China were selected as the study areas. Based on the hydrocarbon microseepage theory, the analysis of crude oi...

In this article, an approach for creating image classification rules using evolutionary operators is described. Classification rules, discovered by application of a genetic algorithm on remote sensing data, are able to identify spectral classes with comparable accuracy to that of a human expert. Particular attention is given to mutation operators and their efficiency in the creation of robust c...

Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most significant application worlds for remote sensing. A few number of image classification...

Hyperspectral remote sensing technique provides fine and detailed spectral information by contiguous and narrow spectral channels. For the traditional classification algorithms, most of them are based on spectral information; spatial information which is useful for the hyperspectral data analysis is paid a little attention to. So, hyperspectral image classification based on effective combinatio...

Analysis of multi/hyperspectral imagery necessitates a selection of an optimal subset of bands in order to avoid the Hughes phenomena and parameter estimation problems due to interband correlation. This can be achieved by employing feature extraction techniques for significant reduction of data dimensionality. From the perspective of statistical pattern recognition, feature extraction refers to...

Recently, real-time image data processing is a popular research area for hyperspectral remote sensing. In particular, target detection surveillance, which is an important military application of hyperspectral remote sensing, demands real-time or near real-time processing. The massive amount of hyperspectral image data seriously limits the processing speed. In this article, a strategy named spat...