Tag: sparsity promotion

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 is as easy as hitting pip install SPICE-HSI in your python terminal or conda install […]

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 forget to check out the paper here!

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 only data points which are functions of class concepts/signatures are available. In particular, this paper […]

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 the Function of Multiple Instances approach (FUMI). The FUMI approach differs significantly from standard Multiple […]

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 (or even thousands) of factors mapped over a region, it is extremely difficult for an […]

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 of bases automatically during the decomposition process. The benefit of sparse promotion for NMF is […]

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: 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 and BISPICE are similar. However, the endmember updates, one novel aspect of the work, are […]

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 number of endmembers. Furthermore, the results shown for this algorithm were generated with the same […]

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 l1 norm and the SPICE algorithm based on the l2 norm, simultaneously and autonomously estimate […]