Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.

/pdf/vertex-component-analysis-a-fast-algorithm-to-unmix-1egulrpm76.pdf

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

/pdf/hyperspectral-unmixing-overview-geometrical-statistical-and-2lb11fr9l4.pdf

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper TM and Enhanced Thematic Mapper Plus ETM+ data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T orthorectified. Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index MODIS EVI time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization FAO land-cover classification system as well as the International Geosphere-Biosphere Programme IGBP system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy OCA of 64.9% assessed with our test samples, with RF 59.8%, J4.8 57.9%, and MLC 53.9% ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples 8629 each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2012.748992

Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

A detailed study of the red edge spectral feature of green vegetation based on laboratory reflectance spectrophotometry is presented. A parameter lambda is defined as the wavelength is defined as the wavelength of maximum slope and found to be dependent on chlorophyll concentration. Species, development stage, leaf layering, and leaf water content of vegetation also influences lambda. The maximum slope parameter is found to be independent of simulated ground area coverage. The results are interpreted in terms of Beer's Law and Kubelka-Munk theory. The chlorophyll concentration dependence of lambda seems to be explained in terms of a pure absorption effect, and it is suggested that the existence of two lambda components arises from leaf scattering properties. The results indicate that red edge measurements will be valuable for assessment of vegetative chlorophyll status and leaf area index independently of ground cover variations, and will be particularly suitable for early stress detection.

The red edge of plant leaf reflectance

■ Spectral imaging for remote sensing of terrestrial features and objects arose as an alternative to high-spatial-resolution, large-aperture satellite imaging systems. Early applications of spectral imaging were oriented toward ground-cover classification, mineral exploration, and agricultural assessment, employing a small number of carefully chosen spectral bands spread across the visible and infrared regions of the electromagnetic spectrum. Improved versions of these early multispectral imaging sensors continue in use today. A new class of sensor, the hyperspectral imager, has also emerged, employing hundreds of contiguous bands to detect and identify a variety of natural and man-made materials. This overview article introduces the fundamental elements of spectral imaging and discusses the historical evolution of both the sensors and the target detection and classification applications.

Spectral Imaging for Remote Sensing

A new, state-of-the-art atmospheric correction algorithm for the solar spectral range has been developed based on the MODTRAN4 code. The primary data products are surface reflectance spectra, column water vapor maps and relative surface elevation maps. In addition, a radiance simulation tool, an automated visibility retrieval algorithm and a spectral 'polishing' algorithm are included. Validations of retrievals have been carried out by analyzing data that encompass a variety of atmospheric and surface conditions. Some results and their implications for atmospheric correction and spectroscopy are discussed.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

https://aviris.jpl.nasa.gov/proceedings/workshops/99_docs/3.pdf

Atmospheric correction for shortwave spectral imagery based on MODTRAN4

This paper presents an overview of the latest version of a MODTRAN4-based atmospheric correction (or "compensation") algorithm developed by Spectral Sciences, Inc. and the Air Force Research Laboratory for spectral imaging sensors. New upgrades to the algorithm include automated aerosol retrieval, cloud masking, and speed improvements. In addition, MODTRAN4 has been updated to correct recently discovered errors in the HITRAN-96 water line parameters. Reflectance spectra retrieved from AVIRIS data are compared with "ground truth" measurements, and good agreement is found.

http://aviris.jpl.nasa.gov/proceedings/workshops/00_docs/matthew_web.pdf

Status of atmospheric correction using a MODTRAN4-based algorithm

■ Hyperspectral imaging sensors are used to detect and identify diverse surface materials, topographical features, and geological features. Because the intervening atmosphere poses an obstacle to the retrieval of surface reflectance data, algorithms exist to compensate the measured signal for the effects of the atmosphere. This article provides an overview and an evaluation of available atmospheric compensation algorithms for the visible-through-shortwave infrared spectral region, including comparison of operational characteristics, input requirements, algorithm limitations, and computational requirements. Statistical models based on empirical in-scene data are contrasted with physicsbased radiative transfer algorithms. The statistical models rely on a priori scene information that is coupled with the sensor spectral observations in a regression algorithm. The physics-based models utilize physical characteristics of the atmosphere to derive water vapor, aerosol, and mixed gas contributions to the atmospheric signal. Treatment of aerosols in atmospheric compensation models varies considerably and is discussed in some detail. A three-band ratio approach is generally used for the retrieval of atmospheric water vapor. For the surfaces tested in this study, the retrieved surface reflectances from the two physics-based algorithms are similar under dry, clear conditions but differ under moist, hazy conditions. Sensitivity of surface-reflectance retrievals to variations in scene characteristics such as the solar zenith angle, atmospheric visibility, aerosol type, and the atmospheric temperature profile is presented in an effort to quantify the limitations of the models.

Compensation of Hyperspectral Data for Atmospheric Effects

In mid-2003, we will fly software to detect science events that will drive autonomous scene selection onboard the New Millennium Earth Observing 1 (EO- 1) spacecraft. This software will demonstrate the potential for future space missions to use onboard decision-making to detect science events and respond autonomously to capture short -lived science events and to downlink only the highest value science data. .

/pdf/autonomous-science-on-the-eo-1-mission-44yqwvxcq9.pdf

Hsiao-hua K. Burke

Papers

Spectral Imaging for Remote Sensing

Atmospheric correction for shortwave spectral imagery based on MODTRAN4

Status of atmospheric correction using a MODTRAN4-based algorithm

Compensation of Hyperspectral Data for Atmospheric Effects

Autonomous Science on the EO-1 Mission