Compressive Hyperspectral Imaging Using Progressive Total Variation

Compressed Sensing (CS) is suitable for remote acquisition of hyperspectral images for earth observation, since it could exploit the strong spatial and spectral correlations, llowing to simplify the architecture of the onboard sensors. Solutions proposed so far tend to decouple spatial and spectral dimensions to reduce the complexity of the reconstruction, not taking into account that onboard sensors progressively acquire spectral rows rather than acquiring spectral channels. For this reason, we propose a novel progressive CS architecture based on separate sensing of spectral rows and joint reconstruction employing Total Variation. Experimental results run on raw AVIRIS and AIRS images confirm the validity of the proposed system.Comment: To be published on ICASSP 2014 proceeding

Strain relaxation in InGaN/GaN micro-pillars evidenced by high resolution cathodoluminescence hyperspectral imaging

A size-dependent strain relaxation and its effects on the optical properties of InGaN/GaN multiple quantum wells (QWs) in micro-pillars have been investigated through a combination of high spatial resolution cathodoluminescence (CL) hyperspectral imaging and numerical modeling. The pillars have diameters (d) ranging from 2 to 150 μm and were fabricated from a III-nitride light-emitting diode (LED) structure optimized for yellow-green emission at ∼560 nm. The CL mapping enables us to investigate strain relaxation in these pillars on a sub-micron scale and to confirm for the first time that a narrow (≤2 μm) edge blue-shift occurs even for the large InGaN/GaN pillars (d > 10 μm). The observed maximum blue-shift at the pillar edge exceeds 7 nm with respect to the pillar centre for the pillars with diameters in the 2–16 μm range. For the smallest pillar (d = 2 μm), the total blue-shift at the edge is 17.5 nm including an 8.2 nm “global” blue-shift at the pillar centre in comparison with the unetched wafer. By using a finite element method with a boundary condition taking account of a strained GaN buffer layer which was neglected in previous simulation works, the strain distribution in the QWs of these pillars was simulated as a function of pillar diameter. The blue-shift in the QWs emission wavelength was then calculated from the strain-dependent changes in piezoelectric field, and the consequent modification of transition energy in the QWs. The simulation and experimental results agree well, confirming the necessity for considering the strained buffer layer in the strain simulation. These results provide not only significant insights into the mechanism of strain relaxation in these micro-pillars but also practical guidance for design of micro/nano LEDs

Experimental, Analytical and Numerical Characterization of Effects of Fiber Waviness Defects in Laminated Composites

Fiber waviness is one of the most common defects observed in reinforced laminated composites which can occur during manufacturing. The effects of waviness on the mechanical responses of laminated composites under the standard mechanical testing are investigated with proposed experiments, analytical and numerical approaches. The damage consequences including kink band formation, crack onset, delamination and fiber fractures are characterized by means of full field digital imaging and acoustic emission techniques. The notch and waviness size effects on the notched composite compressive and tensile strength are studied using a progressive damage approach using the finite element method. A numerical approach based on a combined continuum damage analysis and cohesive zone interlaminar behavior is proposed to simulate the failure initiation and propagation responses. The proposed FE modeling approach will attempt to predict the response of the laminate structure inferring distinctive failure mechanisms and their interactions with the defects. Hybrid near infrared hyperspectral imaging surfaces with a bottom-up design discretization approach have been developed to build the finite element model. The proposed method overcomes the limitations of current wrinkle assessment methods by connecting the high sensitivity near infrared hyperspectral measurements to direct structural models. Temporal evaluations of the load-deformation response, acoustic emissions, and optical microscopy are used to study and verify the failure modes and damage progression models in the tension and compression specimens. An analytical model based on the orthotropic stress concentration factor and a generalized expression using traction continuity through the kink band is developed to predict failure strength of the Open Hole Compression (OHC) specimens. In this thesis, a new methodology to determine the limit point is also proposed based on the out-of-plane displacement tracking using an image correlation method. The method can be used to determine the start of incipient interlaminar delamination in continuous fiber reinforced composite materials

Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstruction

Hyperspectral Image (HSI) reconstruction has made gratifying progress with the deep unfolding framework by formulating the problem into a data module and a prior module. Nevertheless, existing methods still face the problem of insufficient matching with HSI data. The issues lie in three aspects: 1) fixed gradient descent step in the data module while the degradation of HSI is agnostic in the pixel-level. 2) inadequate prior module for 3D HSI cube. 3) stage interaction ignoring the differences in features at different stages. To address these issues, in this work, we propose a Pixel Adaptive Deep Unfolding Transformer (PADUT) for HSI reconstruction. In the data module, a pixel adaptive descent step is employed to focus on pixel-level agnostic degradation. In the prior module, we introduce the Non-local Spectral Transformer (NST) to emphasize the 3D characteristics of HSI for recovering. Moreover, inspired by the diverse expression of features in different stages and depths, the stage interaction is improved by the Fast Fourier Transform (FFT). Experimental results on both simulated and real scenes exhibit the superior performance of our method compared to state-of-the-art HSI reconstruction methods. The code is released at: https://github.com/MyuLi/PADUT.Comment: ICCV 202

Degradation Estimation Recurrent Neural Network with Local and Non-Local Priors for Compressive Spectral Imaging

In the Coded Aperture Snapshot Spectral Imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3D hyperspectral images (HSIs) from 2D measurements. However, some noticeable gaps exist between the imaging model used in DUNs and the real CASSI imaging process, such as the sensing error as well as photon and dark current noise, compromising the accuracy of solving the data subproblem and the prior subproblem in DUNs. To address this issue, we propose a Degradation Estimation Network (DEN) to correct the imaging model used in DUNs by simultaneously estimating the sensing error and the noise level, thereby improving the performance of DUNs. Additionally, we propose an efficient Local and Non-local Transformer (LNLT) to solve the prior subproblem, which not only effectively models local and non-local similarities but also reduces the computational cost of the window-based global Multi-head Self-attention (MSA). Furthermore, we transform the DUN into a Recurrent Neural Network (RNN) by sharing parameters of DNNs across stages, which not only allows DNN to be trained more adequately but also significantly reduces the number of parameters. The proposed DERNN-LNLT achieves state-of-the-art (SOTA) performance with fewer parameters on both simulation and real datasets

Inexact Gradient Projection and Fast Data Driven Compressed Sensing

We study convergence of the iterative projected gradient (IPG) algorithm for arbitrary (possibly nonconvex) sets and when both the gradient and projection oracles are computed approximately. We consider different notions of approximation of which we show that the Progressive Fixed Precision (PFP) and the $(1+\epsilon)$-optimal oracles can achieve the same accuracy as for the exact IPG algorithm. We show that the former scheme is also able to maintain the (linear) rate of convergence of the exact algorithm, under the same embedding assumption. In contrast, the $(1+\epsilon)$-approximate oracle requires a stronger embedding condition, moderate compression ratios and it typically slows down the convergence. We apply our results to accelerate solving a class of data driven compressed sensing problems, where we replace iterative exhaustive searches over large datasets by fast approximate nearest neighbour search strategies based on the cover tree data structure. For datasets with low intrinsic dimensions our proposed algorithm achieves a complexity logarithmic in terms of the dataset population as opposed to the linear complexity of a brute force search. By running several numerical experiments we conclude similar observations as predicted by our theoretical analysis

Application-Dependent Wavelength Selection For Hyperspectral Imaging Technologies

Hyperspectral imaging has proven to provide benefits in numerous application domains, including agriculture, biomedicine, remote sensing, and food quality management. Unlike standard color imagery composed of these broad wavelength bands, hyperspectral images are collected over numerous (possibly hundreds) of narrow wavelength bands, thereby offering vastly more information content than standard imagery. It is this higher information content which enables improved performance in complex classification and regression tasks. However, this successful technology is not without its disadvantages which include high cost, slow data capture, high data storage requirements, and computational complexity. This research seeks to overcome these disadvantages through the development of algorithms and methods to enable the benefits of hyperspectral imaging in inexpensive portable devices that collect spectral data at only a handful (i.e., 5-7) of wavelengths specifically selected for the application of interest.This dissertation focuses on two applications of practical interest: fish fillet species classification for the prevention of food fraud and tissue oxygenation estimation for wound monitoring. Genetic algorithm, self-organizing map, and simulated annealing approaches for wavelength selection are investigated for the first application, combined with common machine learning classifiers for species classification. The simulated annealing approach for wavelength selection is carried over to the wound monitoring application and is combined with the Extended Modified Lambert-Beer law, a tissue oxygenation method that has proven to be robust to differences in melanin concentrations. Analyses for this second application included spectral convolutions to represent data collection with the envisioned inexpensive portable devices. Results of this research showed that high species classification accuracy (\u3e 90%) and low tissue oxygenation error (\u3c 1%) is achievable with just 5-7 selected wavelengths. Furthermore, the proposed wavelength selection and estimation algorithms for the wound monitoring application were found to be robust to variations in the peak wavelength and relatively wide bandwidths of the types of LEDs that may be featured in the designs of such devices