Recent progress in canopy bidirectional reflectance distribution function (BRDF) model inversions has allowed accurate estimates of vegetation biophysical characteristics from remotely sensed multi-angle optical data. Since most current BRDF inversion methods utilize one-dimensional (1-D) models, surface homogeneity within an image pixel is implied. The Advanced Very High Resolution Radiometer ...

This study examined linear spectral unmixing techniques for mapping the variation in crop yield for precision agriculture. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery collected from a grain sorghum field and a cotton field. A pair of crop plant and soil spectra derived from each image was used as endmember spectra to generate...

A snow-cover mapping method accounting for forests (SnowFrac) is presented. SnowFrac uses spectral unmixing and endmember constraints to estimate the snow-cover fraction of a pixel. The unmixing is based on a linear spectral mixture model, which includes endmembers for snow, conifer, branches of leafless deciduous trees and snow-free ground. Model input consists of a land-cover fraction map and...

Multiplex immunohistochemistry (IHC) staining is a new, emerging technique for the detection of multiple biomarkers within a single tissue section. The initial key step in multiplex IHC image analysis in digital pathology is of tremendous clinical importance due to its ability to accurately unmix the IHC image and differentiate each of the stains. The technique has become popular due to its sig...

Mixed pixels, which are inevitable in remote sensing images, often result in a lot of limitations in their applications. A novel approach for mixed pixel’s fully constrained unmixing, Fully Constrained Oblique Subspace Projection (FCOBSP) Linear Unmixing algorithm, is proposed to handle this problem. The Oblique Subspace Projection, in which the signal space is oblique to the background space, ...

In the field of remote sensing, the unmixing of hyperspectral images is usually based on the use of a mixing model. Most existing spectral unmixing methods, used in the reflective range [0.4-2.5 μm], rely on a linear model of endmember reflectances. Nevertheless, such a model supposes the pixels at ground level to be uniformly irradiated and the scene to be flat. When considering a 3D landscape...

By recording a time series of tomographic images, dynamic fluorescence molecular tomography (FMT) allows exploring perfusion, biodistribution, and pharmacokinetics of labeled substances in vivo. Usually, dynamic tomographic images are first reconstructed frame by frame, and then unmixing based on principle component analysis (PCA) or independent component analysis (ICA) is performed to detect a...

Alessandro Mecocci University of Siena Department of Information Engineering Via Roma, 56 53100-Siena Si , Italy Abstract. The problem of input noise affecting the subpixel classification is examined in order to assess its relationship with the output noise. The approach followed in this study was to investigate the output noise level obtained with a least-squares subpixel classification algori...

Reference data (“ground truth”) maps have traditionally been used to assess the accuracy of classification algorithms. These maps typically classify pixels or areas of imagery as belonging to a finite number of ground cover classes, but do not include sub-pixel abundance estimates; therefore, they are not sufficiently detailed to directly assess the performance of spectral unmixing algorithms. ...

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing, by D. Hong, L. Gao, J. Yao, N. Yokoya, Chanussot, U. Heiden, and B. Zhang, IEEE Transactions on Neural Networks Systems, Vol. 33, No. 11, Nov. 2022, pp. 6518–6531.