Chlorophyll-a (Chl-a) concentration is a major indicator of water quality which is harmful to human health. A growing number of studies have focused on the derivation of Chl-a concentration information from hyperspectral sensor data and the identification of best indices for Chl-a monitoring. The objective of this study is to assess the potential of hyperspectral indices to detect Chl-a concent...

Hyperspectral images contain rich and fine spectral information, an improvement of land use/cover classification accuracy is expected from the use of such images. However, the classification methods that have been successfully applied to multispectral data in the past are not as effective as to hyperspectral data. The major cause is that the size of training data set does not correspond to the ...

Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multi-modal hyperspectral sensors, controlled by Dynamic Data Driven Applications Systems (DDDAS) methodology, have attracted growing interest due to their ability to record extended data quickly from the aerial platforms. In thi...

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...

Hyperspectral imaging(HI) is an emerging technology in the field of biomedical engineering which may be used as a non-invasive modality for cancer characterization. In this project, we propose to investigate hyperspectral imaging for the characterization of gastric cancer. The hyperspectral imaging has been used for the detection of various kinds of human cancer; breast, gastric, prostate and t...

Hyperspectral imaging offers new opportunities for inter-person facial discrimination. However, due to the high dimensionality of hyperspectral data, discriminative feature extraction for face recognition is more challenging than 2D images. For dimensionality reduction and feature extraction most of the previous approaches just sub sampled the hyperspectral data [5, 6, 9] or used simple PCA [3]...

the purpose of this study is the fusion of polarimetric sar and hyperspectral images for concealed target detection in forest areas. the proposed method is using the polarimetric and the spectral signatures. a radarsat-2 image and a hyperion image of the city san francisco were chosen for evaluation of the proposed method. the four targets were selected in the forest and the outer areas. three ...

This paper examines several hyperspectral data processing algorithms designed for a distributed computing environment. Due to the large size, hyperspectral data requires long computational times to process. In a distributed environment, the processing can be split into several components, many of them being executed simultaneously, thus leading to increased time efficiency. The algorithms are d...

Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images are extremely high-dimensional, and require on-board processing algorithms able to satisfy near real-time constraints in applications such as wildland fire monitoring, mapping...

Accurate classification of tree-species is essential for sustainably managing forest resources and effectively monitoring species diversity. In this study, we used simultaneously acquired hyperspectral and LiDAR data from LiCHy (Hyperspectral, LiDAR and CCD) airborne system to classify tree-species in subtropical forests of southeast China. First, each individual tree crown was extracted using ...