Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to
estimate abundance fractions of materials present in an image pixel. In order for an LSMA …

With very high spectral resolution, hyperspectral sensors can now uncover many unknown
signal sources which cannot be identified by visual inspection or a priori. In order to account …

Hyperspectral data processing typically demands enormous computational resources in
terms of storage, computation, and input/output throughputs, particularly when real-time …

The authors present a projection pursuit (PP) approach to target detection. Unlike most of
developed target detection algorithms that require statistical models such as linear mixture …

The authors present a linearly constrained minimum variance (TCMV) beamforming
approach to real time processing algorithms for target detection and classification in …

Due to significantly improved spatial and spectral resolution, hyperspectral sensors can now
detect many substances that cannot be resolved by multispectral sensors. However, this …

Linear spectral mixture analysis has been widely used for subpixel detection and mixed
pixel classification. When it is implemented as constrained LSMA, the constraints are …

Independent component analysis (ICA) has shown success in blind source separation and
channel equalization. Its applications to remotely sensed images have been investigated in …

Compressive sensing (CS) is a novel sampling methodology representing a paradigm shift
from conventional data acquisition schemes. The theory of compressive sensing ensures …

Due to significantly improved spectral resolution produced by hyperspectral sensors, the
band-to-band correlation is generally very high and can be removed without loss of crucial …