A novel STAP algorithm based on sparse recovery technique, called CS-STAP, were presented. Instead of using conventional maximum likelihood estimation of covariance matrix, our met...
Ke Sun, Hao Zhang, Gang Li, Huadong Meng, Xiqin Wa...
The main purpose of this paper is to describe available (HPC)based implementations of remotely sensed hyperspectral image processing algorithms on multi-computer clusters, heterog...
In this work we present a software simulator for the performance evaluation of the NDSA (Normalized Differential Spectral Absorption) method at global scale assuming a realistic s...
Scientific Workflows provides a technology that facilitates researchers by allowing them to capture in a machine processable manner the method relating to some research. This incr...
Terence L. van Zyl, Anwar Vahed, Graeme McFerren, ...
We have explored in this paper a framework to test in a quantitative manner the stability of different endmember extraction and spectral unmixing algorithms based on the concept o...
Fermin Ayuso, Javier Setoain, Manuel Prieto, Chris...
Spectral mixture analysis is an important task for remotely sensed hyperspectral data interpretation. In spectral unmixing, both the determination of spectrally pure signatures (e...
DubaiSat-1 is an initiative from Emirates Institution for Advanced Science and Technology (EIAST) to start the first Earth observation satellite program in the United Arab Emirate...
Supervised learners can be used to automatically classify many types of spatially distributed data. For example, land cover classification by hyperspectral image data analysis is ...
High-dimensional data such as hyperspectral imagery is traditionally acquired in full dimensionality before being reduced in dimension prior to processing. Conventional dimensiona...
James E. Fowler, Qian Du, Wei Zhu, Nicolas H. Youn...