Subpixel detection and classification are important in identification and quantification of multicomponent mixtures in remotely sensed data, such as multispectral/hyperspectral images. A recently proposed orthogonal subspace projection (OSP) has shown some success in Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. Ho...

spectral decomposition of time series has a significant role in seismic data processing and interpretation. since the earth acts as a low-pass filter, it changes frequency content of passing seismic waves. conventional representing methods of signals in time domain and frequency domain cannot show time and frequency information simultaneously. time-frequency transforms upgraded spectral decompo...

An effective and lossy compression technique for multispectral and hyperspectral image data minimizes both the spatial and spectral correlations while preserving the spectral characteristics of the data. In this paper, we use two-dimensional wavelet transform and propose an encoding technique for wavelet coefficients. We use KroneckerProduct Gain-Shape Vector Quantization coupled with the gener...

Hyperspectral sensors provide much richer information than comparable multispectral sensors. However currently we do not have sufficient recourses to compute results based on all the gathered information. One way to approach this problem is to perform dimension reduction [1] as pre-processing, i.e to apply a transformation that brings data from a high order dimension to a low order dimension. W...

With the number of channels in the hundreds instead of in the tens hyperspectral imagery possesses much richer spectral information than multispectral imagery .The increased dimensionality of such hyperspectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be used without dimension reduction preprocessing. Supervised classific...

forest classification on the basis of satellite images is a promising technique both for primary map production and for map updating and forest monitoring. for accurate for-est classification into three classes, using mapping by canopy cover density “high spatial resolution satellite images have to be used in order to obtain the required spatial detail” [schneider, 1999]. at the same time, the ...

In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit ...