Subspace-based signal processing traditionally focuses on problems involving a few subspaces. Recently, a number of problems in different application areas have emerged that involve a significantly larger number of subspaces relative to the ambient dimension. It becomes imperative in such settings to first identify a smaller set of active subspaces that contribute to the observation before furt...

Laser-scanning microscopy allows rapid acquisition of multi-channel data, paving the way for high-throughput, high-content analysis of large numbers of images. An inherent problem of using multiple fluorescent dyes is overlapping emission spectra, which results in channel cross-talk and reduces the ability to extract quantitative measurements. Traditional unmixing methods rely on measuring chan...

The quantitative forecasting of hyperspectral system performance is an important capability at every stage of system development including system requirement definition, system design, and sensor operation. In support of this, Lincoln Laboratory has been developing an analytical modeling tool to predict end-to-end spectroradiometric remote sensing system performance. Recently, the model has bee...

Pixel unmixing is commonly performed by employing a least squared (LS) error criterion, making it sensitive to outliers. As an alternative, the least median of squares (LMedS) method is proposed. Not only is it extremely robust, but it is efficient and straightforward both to implement and use.

Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With superior performance, transformers found way field of hyperspectral image classification and achieved promising results. In this article, we harness power to conquer task unmixing propose a novel deep neural network-based model transformers. A transformer networ...

expensive to provide these maps  with field measurements  therefore  it is better to use new methods. this study provides a lithological and alteration mapping units with dominant minerals based on hyperspectral images of eo1-hyperion satellite. to do so, two different zones  were  investigated:  the  cuprite-nevada  and  mozahem  volcano  in  iran  which  have suitable conditions for our study...

Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions...

Accurate evaluation of metastatic lymph nodes (LNs) is indispensable for adequate treatment of colorectal cancer (CRC) patients. Here, we demonstrate detection of metastases of human CRC in removed fresh LNs using 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence. A spectral unmixing method was employed to reduce the overlap of collagen autofluorescence on PpIX fluoresce...

Biochar soil amendment is globally recognized as an emerging approach to mitigate CO2 emissions and increase crop yield. Because the durability and changes of biochar may affect its long term functions, it is important to quantify biochar in soil after application. In this chapter, an automatic soil biochar estimation method is proposed by analysis of hyperspectral images captured by cameras th...