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Hyperspectral unmixing frameworks are ultimately designed to understand and quantify the actual distribution of endmembers in a given scene. Assessing the percentage of each material is typically cumbersome, especially in images characterized by complex combinations of spectral signatures. In this work, we present a nonlinear programming scheme that aims at providing direct estimation of the endmembers...
Reference data (“ground truth”) maps are commonly used to quantitatively assess the performance of imaging spectrometer classification algorithms. However, standard reference data scenes typically are not sufficiently detailed to support assessment of spectral unmixing algorithms. Furthermore, commonly used reference data often lack validation reports that estimate error in the reference data itself,...
Endmember extraction is a fundamental task in spectral unmixing of remotely sensed hyperspectral images. In this work, we develop a new robust algorithm for endmember extraction which is based on a nonnegative sparse autoencoder. The proposed approach is based on two main steps. First, it uses an automatic sampler approach with local outlier factor and affinity propagation to intelligently gather...
Spectral unmixing is to decompose the hyperspectral data into endmembers and abundances. It has been known to be a challenging and ill-posed task due to the corruption of noise as well as complex environmental conditions. In this paper, we propose a part-based denoising autoencoder with unique structure that solves the unmixing challenges. The effective l21 norm and denoising constraints are applied...
Several classes of endmember (EM) extraction algorithms based on the pure pixel assumption exist. Most of these algorithms employ some geometrical interpretation of the spectral mixing process, and use orthogonal projections, random projections, or some combination of them. Random projection based algorithms, such as pixel purity index, often find clusters of EM candidates which show high correlation,...
In this paper, we present a fast blind multitemporal hyperspectral unmixing algorithm, using an l1 penalty to promote sparse abundances. The method is able to account for different acquisition conditions of multitemporal images, by allowing the spectral signatures in the different temporal images to vary. The new algorithm is tested on simulated data and applied on real hyperspectral data.
Spectral Unmixing is a challenging and absorbing problem. Unmixning allows us to break down a pixel's composition into its material components. Many avenues of spectral unmixing have been attempted with considerable success. One such avenue is to frame the spectral unmixing problem as an Estimation-Measurement problem and avail the use of the well-known Kalman Filter (KF) technique. Two such recent...
Sparse unmixing of hyperspectral data is an important technique which aims at estimating the fractional abundances of endmembers (pure spectral components). It is well known that enforcing sparseness becomes a necessary process in sparse unmixing methods. To better exploit the sparsity in hyperspectral imagery, a double reweighted sparse unmixing algorithm has been proposed. However, it focusses on...
In this paper, hyperspectral data is modeled as a combination of a sparse component, a low rank component and noise. The low rank component is a product of the endmembers and the abundances in an image, and the sparse component is composed of outliers and structured noise. Outliers and structured noise in this context are, e.g. band specific noise, vertical or horizontal artifacts or saturated pixels...
We propose to use the temporal coherence of a time series to extract using Vertex Component Analysis (VCA) the suitable set of endmembers for each scene. The reconstruction error computed on the two previous scenes for each date is used to constrain the selection of the set of endmembers produced by VCA. Snow cover estimation is considered as application. We tested different approaches for abundance...
The Ultra-Wideband Microwave Radiometer is a novel pseudo-correlation radiometer design measuring scene brightness temperatures from 0.5–2 GHz created under NASA's Instrument Incubator Program. This document analyzes the design and operation of the radiometer, the accuracy and stability of the brightness temperatures it produces, and presents initial results from a field campaign conducted in Greenland...
In this paper we discuss a polarimateric calibration technique applied on the Soil Moisture Active Passive (SMAP) L-band radiometer. We take advantage of the SMAP antenna rotation and varying incidence angle during pitch maneuvers performed by the spacecraft for periodic cold-sky calibration. We present initial comparisons between the polarization corrected ocean signal at various incidence angles...
Recently, the advanced coastline inflection point method (CIPSD) was developed to retrieve the instrument boresight pointing error of Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS). Because of the use of mathematical model and separate-domain technique, this algorithm can retrieve the boresight pointing error in terms of Euler angles efficiently. But...
The Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to produce unified precipitation retrievals from a constellation of available microwave radiometers [1]. The core observatory builds on the successes of the Tropical Rainfall Measuring Mission (TRMM) providing advances in satellite precipitation monitoring including a dual-frequency radar, increased...
The laboratory calibration of airborne Hurricane Imaging Radiometer's C-Band multi-frequency receivers is described here. The method used to obtain the values of receiver front-end loss and injected noise diode temperature is presented along with the expected RMS uncertainty in the final calibration. Internal Warm load was excluded from the calibration due to an apparent anomaly.
ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 7 years, with its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. This 7 year period has provided a wealth of information which has enabled us to understand and consolidate the performance of the payload in great detail. More importantly, we know now the things that work well, those that...
This paper introduces a multi-satellite approach to passive microwave interferometric radiometry applied to geostationary atmospheric sounding at 53 GHz. The concept applies satellite formation flight to the currently operational interferometric techniques to extend the achievable microwave aperture sizes, leading to unprecedented spatial resolution for microwave radiometers. The presented configurations...
The impact of Radio Frequency Interference (RFI) is a very serious problem for spaceborne microwave radiometry. Many Soil Moisture Ocean Salinity (SMOS) images show serious contamination by the RFI. SMOS is even more impacted by the RFI than real aperture case because the grating lobes are usually higher in Synthetic Aperture Interferometric Radiometer (SAIR) compared to real aperture one. RFI effects...
Recent advances in low-power millimeterwave low-noise amplifier technologies have enabled the hosting of high-performance atmospheric sounding instruments on very small satellites. Microwave instrumentation is particularly well suited for such implementations, as the sensor requirements for power, pointing, and spatial resolution (aperture size) can readily be accommodated by a nanosatellite platform...
The Stepped Frequency Microwave Radiometer (SFMR) is an instrument flown on research and reconnaissance aircraft through tropical and extratropical cyclones providing rain rate and surface wind speed estimates. Errors have been observed with the retrievals from SFMR, especially in extratropical cyclones over cold water, when compared with other sensors. In this paper, some of the SFMR wind speed errors...
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