Sparsity Promoting Iterated Constrained Endmemebers (SPICE) is now installable with conda! SPICE is an algorithm for finding hyperspectral endmembers and corresponding proportions for a scene. The Python implementation can now be installed easily from PyPI or through the conda-forge. Installation… Read More
Tag: sparsity promotion
PYTHON JUST GOT SPICE-Y!
Sparsity Promoting Iterated Constrained Endmemebers (SPICE) is now in the Python Package Index! SPICE is an efficient algorithm for finding hyperspectral endmembers and corresponding proportions for a scene. The Python implementation can now be installed easily from PyPI. Also, don’t… Read More
Functions of Multiple Instances for Learning Target Signatures
Abstract: The functions of multiple instances (FUMI) approach for learning target and nontarget signatures is introduced. FUMI is a generalization of the multiple-instance learning (MIL) approach for supervised learning. FUMI differs significantly from standard MIL and supervised learning approaches because… Read More
Functions of multiple instances for sub-pixel target characterization in hyperspectral imagery
Abstract: In this paper, the Multi-target Extended Function of Multiple Instances (Multi-target eFUMI) method is developed and described. The method is capable of learning multiple target spectral signatures from weakly- and inaccurately-labeled hyperspectral imagery. Multi-target eFUMI is a generalization of… Read More
Endmember representation of human geography layers
Abstract: This paper presents an endmember estimation and representation approach for human geography data cubes. Human-related factors that can be mapped for a geographic region include factors relating to population, age, religion, education, medical access and others. Given these hundreds… Read More
Sparsity promoted non-negative matrix factorization for source separation and detection
Abstract: The effectiveness of non-negative matrix factorization (NMF) depends on a suitable choice of the number of bases, which is often difficult to decide in practice. This paper imposes sparseness on the factorization coefficients in order to determine the number… Read More
Hyperspectral unmixing and band weighting for multiple endmember sets
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… Read More
A sparsity promoting bilinear unmixing model
Abstract: An algorithm, Bilinear SPICE (BISPICE), for simultaneously estimating the number of endmembers, the endmembers, and proportions for a bilinear mixing model is derived and evaluated. BISPICE generalizes the SPICE algorithm for linear mixing. The proportion estimation steps of SPICE… Read More
Robust endmember detection using L1 norm factorization
Abstract: The results from L1-Endmembers display the algorithm’s stability and accuracy with increasing levels of noise. The algorithm was extremely stable in the number of endmembers when compared to the SPICE algorithm and the Virtual Dimensionality methods for estimating the… Read More
L1-endmembers: a robust endmember detection and spectral unmixing algorithm
Abstract: A hyperspectral endmember detection and spectral unmixing algorithm based on an l1 norm factorization of the input hyperspectral data is developed and compared to a method based on l2 norm factorization. Both algorithms, the L1-Endmembers algorithm based on the… Read More