This tutorial is designed to provide an overview of selected methods for analysis of imaging spectrometer data. "Calibration" to reflectance is a prerequisite for most analysis approaches. A brief review of both empirical and model-based methods for recovery of apparent surface reflectance from the data is presented. Data analysis methods discussed include single pixel spectrum analysis, both e...

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spec...

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spec...

Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain several materials. The process of determining the abundance of representative materials in a single pixel is called spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman iteration. We then use the unmixing information and Total Variatio...

Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. New algorithms have been developed for hyperspectral data classification lately, but also traditional classification algorithms have often been used. This study compares different classification algorithms for classification of vegetation using imaging spectrometer da...

In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral image (MSI) of the same scene and high resolution images from unrelated scenes. The fusion method is bas...

The mapping of hydrothermal alteration zones associated with epithermal gold deposits on the island of Lesvos in Greece has been carried out using Landsat Thematic Mapper (TM) satellite and ground remote sensing data. The initial analysis of the satellite data using the minimum noise fraction, matched filter and spectral unmixing techniques identified the altered rock outcrops clearly. The iden...

This paper proposes a novel algorithm for the recovery of noisy bands from hyperspectral images. The method, based on spectral unmixing, relies on the spectral behavior of the materials on ground composing each image element. Firstly, reference spectra related to the classes of interest are used to perform spectral unmixing: these exhibit negligible noise influences as they are averaged over ar...

Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can benefit from spatial regularization strategies. While existing spatial regularization methods improve the problem conditioning and promote piecewise smooth solutions, ...