Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in conjunction with JPEG2000 for hyperspectral-image compression. However, the computational cost of determining the data-dependent PCA transform is high due to its traditional eigendecomposition implementation which requi...

1.0 Introduction Imaging Spectrometry data or Hyperspectral Imagery (HSI) acquired using airborne systems have been used in the geologic community since the early 1980’s and represent a mature technology (Goetz et al., 1985; Kruse et al., 1999). The solar spectral range, 0.4 to 2.5 μm, provides abundant information about many important Earth-surface minerals (Clark et al., 1990). In particular,...

30 Abstract—The blessings of hyperspectral remote sensing are manifold and it has enabled researchers to locate, map and identify different materials on the surface of Earth. Hyperspectral remote sensing play a key role in mineral mapping activities and it can be a much powerful and cost effective tool for mineral development activities in a developing country like Pakistan where there are rich...

An unsupervised neuro-fuzzy system, Gaussian fuzzy self-organizing map (GFSOM), is proposed for hyperspectral image classification. This algorithm operates by integrating an unsupervised neural network with a Gaussian function-based fuzzy system. We also explore the potential for hyperspectral image analysis of three other artificial intelligence (AI)-based unsupervised techniques popular for m...

Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle) platforms (<20 kg) are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to strin...

Oil spill is one of major marine environmental challenges. The main impacts of this phenomenon are preventing light transmission into the deep water and oxygen absorption, which can disturb the photosynthesis process of water plants. In this research, we utilize SpecTIR airborne sensor data to extract and classify oils spill for the Gulf of Mexico Deepwater Horizon (DWH) happened in 2010. For t...

We have developed a new and innovative technique for combining a high-spatial-resolution multispectral image with a lower-spatial-resolution hyperspectral image. The approach, called CRISP, compares the spectral information present in the multispectral image to the spectral content in the hyperspectral image and derives a set of equations to approximately transform the multispectral image into ...

A method is presented for sub-pixel mapping and classification in hyperspectral imagery, using learned blockstructured discriminative dictionaries, where each block is adapted and optimized to represent a material in a compact and sparse manner. The spectral pixels are modeled by linear combinations of subspaces defined by the learned dictionary atoms, allowing for linear mixture analysis. This...

A method is presented for sub-pixel mapping and classification in hyperspectral imagery, using learned blockstructured discriminative dictionaries, where each block is adapted and optimized to represent a material in a compact and sparse manner. The spectral pixels are modeled by linear combinations of subspaces defined by the learned dictionary atoms, allowing for linear mixture analysis. This...

Target temperature estimation from thermal infrared (TIR) imagery is a complex task that becomes increasingly more difficult as the target size approaches the size of a projected pixel. At that point the assumption of pixel homogeneity is invalid as the radiance value recorded at the sensor is the result of energy contributions from the target material and any other background material that fal...