3 research outputs found
Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing
An efficient spatial regularization method using superpixel segmentation and
graph Laplacian regularization is proposed for sparse hyperspectral unmixing
method. Since it is likely to find spectrally similar pixels in a homogeneous
region, we use a superpixel segmentation algorithm to extract the homogeneous
regions by considering the image boundaries. We first extract the homogeneous
regions, which are called superpixels, then a weighted graph in each superpixel
is constructed by selecting -nearest pixels in each superpixel. Each node in
the graph represents the spectrum of a pixel and edges connect the similar
pixels inside the superpixel. The spatial similarity is investigated using
graph Laplacian regularization. Sparsity regularization for abundance matrix is
provided using a weighted sparsity promoting norm. Experimental results on
simulated and real data sets show the superiority of the proposed algorithm
over the well-known algorithms in the literature.Comment: 5 page
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model
Hyperspectral unmixing is an important remote sensing task with applications
including material identification and analysis. Characteristic spectral
features make many pure materials identifiable from their visible-to-infrared
spectra, but quantifying their presence within a mixture is a challenging task
due to nonlinearities and factors of variation. In this paper, spectral
variation is considered from a physics-based approach and incorporated into an
end-to-end spectral unmixing algorithm via differentiable programming. The
dispersion model is introduced to simulate realistic spectral variation, and an
efficient method to fit the parameters is presented. Then, this dispersion
model is utilized as a generative model within an analysis-by-synthesis
spectral unmixing algorithm. Further, a technique for inverse rendering using a
convolutional neural network to predict parameters of the generative model is
introduced to enhance performance and speed when training data is available.
Results achieve state-of-the-art on both infrared and visible-to-near-infrared
(VNIR) datasets, and show promise for the synergy between physics-based models
and deep learning in hyperspectral unmixing in the future.Comment: 36 pages, 11 figures. Accepted to European Conference on Computer
Vision (ECCV) 202
Material Based Object Tracking in Hyperspectral Videos: Benchmark and Algorithms
Traditional color images only depict color intensities in red, green and blue
channels, often making object trackers fail in challenging scenarios, e.g.,
background clutter and rapid changes of target appearance. Alternatively,
material information of targets contained in a large amount of bands of
hyperspectral images (HSI) is more robust to these difficult conditions. In
this paper, we conduct a comprehensive study on how material information can be
utilized to boost object tracking from three aspects: benchmark dataset,
material feature representation and material based tracking. In terms of
benchmark, we construct a dataset of fully-annotated videos, which contain both
hyperspectral and color sequences of the same scene. Material information is
represented by spectral-spatial histogram of multidimensional gradient, which
describes the 3D local spectral-spatial structure in an HSI, and fractional
abundances of constituted material components which encode the underlying
material distribution. These two types of features are embedded into
correlation filters, yielding material based tracking. Experimental results on
the collected benchmark dataset show the potentials and advantages of material
based object tracking.Comment: Update result