Browsing by Author "Chatterjee, Ayan"
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Item Open Access Design of a tunable snapshot multispectral imaging system through ray tracing simulation(MDPI, 2019-01-05) Ding, Mengjia; Yuen, Peter W. T.; Piper, Jonathan; Godfree, Peter; Chatterjee, Ayan; Zahidi, Usman; Selvagumar, Senthurran; James, David; Richardson, Mark A.Research on snapshot multispectral imaging has been popular in the remote sensing community due to the high demands of video-rate remote sensing system for various applications. Existing snapshot multispectral imaging techniques are mainly of a fixed wavelength type, which limits their practical usefulness. This paper describes a tunable multispectral snapshot system by using a dual prism assembly as the dispersion element of the coded aperture snapshot spectral imagers (CASSI). Spectral tuning is achieved by adjusting the air gap displacement of the dual prism assembly. Typical spectral shifts of about 1 nm at 400 nm and 12 nm at 700 nm wavelength have been achieved in the present design when the air-gap of the dual prism is changed from 4.24 mm to 5.04 mm. The paper outlines the optical designs, the performance, and the pros and cons of the dual-prism CASSI (DP-CASSI) system. The performance of the system is illustrated by TraceProTM ray tracing, to allow researchers in the field to repeat or to validate the results presented in this paper.Item Open Access Endmember learning with k-means through SCD model in hyperspectral scene reconstructions(MDPI, 2019-11-15) Chatterjee, Ayan; Yuen, Peter W. T.This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary Du" role="presentation" style="max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">Du . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim packageItem Open Access Linear Inverse Problem (LIP) Optimisation for Remote Sensing Applications - Spectral Reconstruction(Cranfield University, 2018-11-15 13:26) Chatterjee, Ayan3MT presented at the 2018 Defence and Security Doctoral Symposium.Remote sensing applications like classification and target detection, particularly for high demanded applications such as the detection of difficult targets from cluttered scene, depends on relevant wavelengths of information. While multispectral imagery in airborne or spaceborne platforms consists of a few wavelengths far apart from each other (usually less than 20 bands), which is found not sufficient enough. This project explores new concepts for using not only spatial/spectral information, but also to extract new information from the few bands in the input data for an enhanced spectral mixture analysis.Item Open Access A radiative transfer model-based multi-layered regression learning to estimate shadow map in hyperspectral images(MDPI, 2019-08-06) Zahidi, Usman A.; Chatterjee, Ayan; Yuen, Peter W. T.The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under specialized constraint by means of in-scene white and black calibration panels. The reflectance of material is invariant to illumination. Exploiting this property, we articulated a mathematical formulation based on the RT model to create cost functions relating variably illuminated regions within a scene. In this paper, we propose multi-layered regression learning-based recovery of radiance components, i.e., total ground-reflected radiance and path radiance from reflectance and radiance images of the scene. These decomposed components represent terms in the RT equation and enable us to relate variable illumination. Therefore, we assume that Hyperspectral Image (HSI) radiance of the scene is provided and AC can be processed on it, preferably with QUick Atmospheric Correction (QUAC) algorithm. QUAC is preferred because it does not account for surface models. The output from the proposed algorithm is an intermediate map of the scene on which our mathematically derived binary and multi-label threshold is applied to classify shadowed and non-shadowed regions. Results from a satellite and airborne NADIR imagery are shown in this paper. Ground truth (GT) is generated by ray-tracing on a LIDAR-based surface model in the form of contour data, of the scene. Comparison of our results with GT implies that our algorithm’s binary classification shadow maps outperform other existing shadow detection algorithms in true positive, which is the detection of shadows when it is in ground truth. It also has the lowest false negative i.e., detecting non-shadowed region as shadowed, compared to existing algorithms.Item Open Access Rapid estimation of orthogonal matching pursuit representation(IEEE, 2021-02-17) Chatterjee, Ayan; Yuen, Peter W. T.Orthogonal Matching Pursuit (OMP) has proven itself to be a significant algorithm in image and signal processing domain in the last decade to estimate sparse representations in dictionary learning. Over the years, efforts to speed up the OMP algorithm for the same accuracy has been through variants like generalized OMP (g-OMP) and fast OMP (f-OMP). All of these algorithms solve OMP recursively for each signal sample among ‘S’ number of samples. The proposed rapid OMP (r-OMP) runs the loop for ‘N’ atoms, simultaneously estimating for all samples, and, in a real scene since N≪S , the proposed approach speeds up OMP by several orders of magnitude. Experiment on a real scene with a popular dictionary learning algorithm, K-SVD, show that the proposed r-OMP completes K-SVD in ≈4% of the computational time compared to using OMPItem Open Access Robust hyperspectral image reconstruction for scene simulation applications(2020-06-10) Chatterjee, Ayan; Yuen, Peter W. T.This thesis presents the development of a spectral reconstruction method for multispectral (MSI) and hyperspectral (HSI) applications through an enhanced dictionary learning and spectral unmixing methodologies. Earth observation/surveillance is largely undertaken by MSI sensing such as that given by the Landsat, WorldView, Sentinel etc, however, the practical usefulness of the MSI data set is very limited. This is mainly because of the very limited number of wave bands that can be provided by the MSI imagery. One means to remedy this major shortcoming is to extend the MSI into HSI without the need of involving expensive hardware investment. Specifically, spectral reconstruction has been one of the most critical elements in applications such as Hyperspectral scene simulation. Hyperspectral scene simulation has been an important technique particularly for defence applications. Scene simulation creates a virtual scene such that modelling of the materials in the scene can be tailored freely to allow certain parameters of the model to be studied. In the defence sector this is the most cost-effective technique to allow the vulnerability of the soldiers/vehicles to be evaluated before they are deployed to a foreign ground. The simulation of a hyperspectral scene requires the details of materials in the scene, which is normally not available. Current state-of-the-art technology is trying to make use of the MSI satellite data, and to transform it into HSI for the hyperspectral scene simulation. One way to achieve this is through a reconstruction algorithm, commonly known as spectral reconstruction, which turns the MSI into HSI using an optimisation approach. The methodology that has been adopted in this thesis is the development of a robust dictionary learning to estimate the endmember (EM) robustly. Once the EM is found the abundance of materials in the scene can be subsequently estimated through a linear unmixing approach. Conventional approaches to the material allocation of most Hyperspectral scene simulator has been using the Texture Material Mapper (TMM) algorithm, which allocates materials from a spectral library (a collection of pre-compiled endmember iii iv materials) database according to the minimum spectral Euclidean distance difference to a candidate pixel of the scene. This approach has been shown (in this work) to be highly inaccurate with large scene reconstruction error. This research attempts to use a dictionary learning technique for material allocation, solving it as an optimisation problem with the objective of: (i) to reconstruct the scene as closely as possible to the ground truth with a fraction of error as that given by the TMM method, and (ii) to learn materials which are trace (2-3 times the number of species (i.e. intrinsic dimension) in the scene) cluster to ensure all material species in the scene is included for the scene reconstruction. Furthermore, two approaches complementing the goals of the learned dictionary through a rapid orthogonal matching pursuit (r-OMP) which enhances the performance of the orthogonal matching pursuit algorithm; and secondly a semi-blind approximation of the irradiance of all pixels in the scene including those in the shaded regions, have been proposed in this work. The main result of this research is the demonstration of the effectiveness of the proposed algorithms using real data set. The SCD-SOMP has been shown capable to learn both the background and trace materials even for a dictionary with small number of atoms (≈10). Also, the KMSCD method is found to be the more versatile with overcomplete (non-orthogonal) dictionary capable to learn trace materials with high scene reconstruction accuracy (2x of accuracy enhancement over that simulated using the TMM method. Although this work has achieved an incremental improvement in spectral reconstruction, however, the need of dictionary training using hyperspectral data set in this thesis has been identified as one limitation which is needed to be removed for the future direction of research.Item Open Access Sample selection with SOMP for robust basis recovery in sparse coding dictionary learning(IEEE, 2019-09-03) Chatterjee, Ayan; Yuen, Peter W. T.Sparse Coding Dictionary (SCD) learning is to decompose a given hyperspectral image into a linear combination of a few bases. In a natural scene, because there is an imbalance in the abundance of materials, the problem of learning a given material well is directly proportional to its abundance in the training scene. By a random selection of pixels to train a given dictionary, the probability of bases learning a given material is proportional to its distribution in the scene. We propose to use SOMP residue for sample selection with each iteration for a more robust or ‘more complete’ learning. Experiments show that the proposed method learns from both background and trace materials accurately with over 0.95 in Pearson correlation coefficient. Furthermore, the proposed implementation has resulted in considerable improvements in Target Detection with Adaptive Cosine Estimator (ACE).