Tag: band selection

Congratulations to our labmates and collaborators: Ricardo Augusto Borsoi, Tales Imbiriba, Jose Carlos Moreira Bermudez, Cedric Richard, Jocelyn Chanussot, Lucas Drumets, Jean-Yves Tourneret, Alina Zare and Christian Jutten!  Their publication, “Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review” was recently accepted to the IEEE Geoscience and Remote Sensing Magezine. In their paper, the authors […]

Abstract: The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process […]

Congratulations to our labmates Ron Fick and Susan Meerdink for being accepted to the 2019 IEEE WHISPERS conference in Amsterdam! The WHISPERS  conference is an annual workshop focusing on advances in remote sensing with hyperspectral data.  Ron will present on his paper titled “Temporal mapping of Hyperspectral Data”. Susan will present a poster of her […]

Abstract: Traditional methods of developing spectral libraries for unmixing hyperspectral images tend to require domain knowledge of the study area and the material’s spectra. In this paper, we propose using the Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator (Multi-Target MI-ACE) algorithm to develop spectral libraries that will capture the same spectral variability as traditional methods […]

Abstract: Imaging spectrometers measure the response from materials across the electromagnetic spectrum. Often, in remote sensing applications, the imaging spectrometers have low spectral resolution resulting in most measurements being mixed spectra from a scene. In these cases, pixels are assumed to be mixtures of pure spectra known as endmembers. Given the prevalence of mixed spectra, […]

Abstract: In this paper, the SimUltaneous Band-weighting and Spectral Unmixing for Multiple Endmember Sets (SUBSUME) which performs endmember extraction for multiple sets of endmembers, estimates proportion values, and assigns partition-specific band weights is presented. By incorporating simultaneous band weighting, input hyperspectral data is partitioned while focusing on spectral information from the wavelengths that provide the […]

Abstract: Four methods of endmember detection and spectral unmixing are described. The methods determine endmembers and perform spectral unmixing while simultaneously determining the number of endmembers, representing endmembers as distributions, partitioning the input data set into several convex regions, or performing hyperspectral band selection. Few endmember detection algorithms estimate the number of endmembers in addition […]

Abstract: This letter presents a simultaneous band selection and endmember detection algorithm for hyperspectral imagery. This algorithm is an extension of the sparsity promoting iterated constrained endmember (SPICE) algorithm. The extension adds spectral band weights and a sparsity promoting prior to the SPICE objective function to provide integrated band selection. In addition to solving for […]

Abstract: An extension of the iterated constrained endmembers (ICE) that incorporates sparsity promoting priors to find the correct number of endmembers and simultaneously select informative spectral bands is presented. In addition to solving for endmembers and endmember fractional maps, this algorithm attempts to autonomously determine the number of endmembers required for a particular scene. The […]