A New Method to Change Illumination Effect Reduction Based on Spectral Angle Constraint for Hyperspectral Image Unmixing

Rabah, Zouhaier Ben; Farah, Imed Riadh; Mercier, Grégoire; Solaiman, Basel

doi:10.1109/lgrs.2011.2157890

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…Reference pseudospectra can be easily generated for each unique imaging experiment, making pSAM a desirable method for classifying pixels and cells in high-plex images. Additionally, while not demonstrated in this work, vector similarity metrics can also be used for spectral unmixing [36]. In densely packed regions of cells, different cell types can have overlapping signal at the cell membrane [37].…”

Section: Discussionmentioning

confidence: 99%

Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data

Durkee,

Ai,

Casella

et al. 2024

Preprint

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The rapid development of highly multiplexed microscopy systems has enabled the study of cells embedded within their native tissue, which is providing exciting insights into the spatial features of human disease [1]. However, computational methods for analyzing these high-content images are still emerging, and there is a need for more robust and generalizable tools for evaluating the cellular constituents and underlying stroma captured by high-plex imaging [2]. To address this need, we have adapted spectral angle mapping – an algorithm used widely in hyperspectral image analysis – to compress the channel dimension of high-plex immunofluorescence images. As many high-plex immunofluorescence imaging experiments probe unique sets of protein markers, existing cell and pixel classification models do not typically generalize well. Pseudospectral angle mapping (pSAM) uses reference pseudospectra – or pixel vectors – to assign each pixel in an image a similarity score to several cell class reference vectors, which are defined by each unique staining panel. Here, we demonstrate that the class maps provided by pSAM can directly provide insight into the prevalence of each class defined by reference pseudospectra. In a dataset of high-plex images of colon biopsies from patients with gut autoimmune conditions, sixteen pSAM class representation maps were combined with instance segmentation of cells to provide cell class predictions. Finally, pSAM detected a diverse set of structure and immune cells when applied to a novel dataset of kidney biopsies imaged with a 43-marker panel. In summary, pSAM provides a powerful and readily generalizable method for evaluating high-plex immunofluorescence image data.Significance StatementUnderstanding the cellular constituents captured by highly multiplexed tissue imaging is a major limitation affecting the usability of these novel imaging methods. Many imaging experiments have uniquely designed staining panels, reducing the generalizability of cell classification models to new datasets. We present pseudospectral angle mapping (pSAM), which can compress high-dimensional image data into class representations. We demonstrate that the class representations generated by pSAM can be used to interpret high-plex image data and guide cell classification. Importantly, we also demonstrate that pSAM can generalize to new image datasets—collected with a different staining panel in samples from different tissues—without manual image annotation, subjective intensity gating, or re-training an algorithm.

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Section: Discussionmentioning

confidence: 99%

Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data

Durkee,

Ai,

Casella

et al. 2024

Preprint

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“…The main limitation of Euclidean norm is that it aggravates the method by estimating inappropriate/underestimated endmembers under severe illumination changes and nonlinearity (i.e., spectral variability) [63]. On the other hand, the spectral angle distance-like (SAD) operator overcomes these limitations and improves unmixing performance by exploiting geometric features of samples as explained in [30], [64]. In addition, smoothness and sparsity priors should be considered in the loss function for better parameter convergence.…”

Section: A Sparse Autoencoder For Hyperspectral Unmixingmentioning

confidence: 99%

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

Özkan

Kaya

Akar

2019

IEEE Trans. Geosci. Remote Sensing

161

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Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions. Spectral unmixing is a technique that allows us to obtain the material spectral signatures and their fractions from hyperspectral data. In this paper, we propose a novel endmember extraction and hyperspectral unmixing scheme, so called EndNet, that is based on a two-staged autoencoder network. This wellknown structure is completely enhanced and restructured by introducing additional layers and a projection metric (i.e., spectral angle distance (SAD) instead of inner product) to achieve an optimum solution. Moreover, we present a novel loss function that is composed of a Kullback-Leibler divergence term with SAD similarity and additional penalty terms to improve the sparsity of the estimates. These modifications enable us to set the common properties of endmembers such as non-linearity and sparsity for autoencoder networks. Lastly, due to the stochastic-gradient based approach, the method is scalable for large-scale data and it can be accelerated on Graphical Processing Units (GPUs). To demonstrate the superiority of our proposed method, we conduct extensive experiments on several well-known datasets. The results confirm that the proposed method considerably improves the performance compared to the state-of-the-art techniques in literature.

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“…Spectral bands for which VIP < VIP 0 can contain a non-negligible additional information and, therefore, contribute to increase the coefficient of determination R 2 . The classical PLSR gives the same importance to all the spectral bands while the VIP selection method gives importance to the retained spectral bands and removes the importance of the eliminated bands [14].…”

Section: Soil Properties Modeling and Assessmentmentioning

confidence: 99%

Hyperspectral Analysis for a Robust Assessment of Soil Properties Using Adapted PLSR Method

Rabah¹,

Garbia²,

Karray³

et al. 2019

ARS

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Near-InfraRed and Visible (Vis-NIR) spectroscopy is a promising tool allowing to quantify soil properties. It shows that information encoded in hyperspectral data can be useful after signal processing and model calibration steps, in order to estimate various soil properties throughout appropriate statistical models. However, one of the problems encountered in the case of hyperspectral data is related to information redundancy between different spectral bands. This redundancy is at the origin of multi-collinearity in the explanatory variables leading to unstable regression coefficients (and, difficult to interpret). Moreover, in hyperspectral spectrum, the information concerning the chemical specificity is spread over several wavelengths. Therefore, it is not wise to remove this redundancy because this removal affects both relevant and irrelevant hyperspectral information. In this study, the faced challenge is to optimize the estimation of some soil properties by exploiting all the spectral richness of the hyperspectral data by providing complementary rather than redundant information. To this end, a new reliable approach based on hyperspectral data analysis and partial least squares regression is proposed.

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A New Method to Change Illumination Effect Reduction Based on Spectral Angle Constraint for Hyperspectral Image Unmixing

Cited by 13 publications

References 17 publications

Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data

Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

Hyperspectral Analysis for a Robust Assessment of Soil Properties Using Adapted PLSR Method

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