High Resolution Hyperspectral Image Research Articles

Semi-blind hyperspectral and multispectral image fusion via generalized inverse matrix optimization

Combining a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI) is an economical way to obtain a high spatial resolution hyperspectral image (HR-HSI). However, existing fusion methods often rely on a critical variable, the point spread function (PSF), which is difficult to obtain in practice. Additionally, these methods often require a long fusion time and have many parameters that need re-debugging on different datasets to achieve satisfactory results. Many existing methods lack robustness in handling complex scenarios, such as complex PSF, changing spatial downsampling factors, and large-scale datasets. These issues can result in degraded performance or render the methods inapplicable to practical scenarios. Therefore, we propose an efficient and parameterless semi-blind fusion method based on generalized inverse matrix optimization (GIMO), which is robust for multiple complex situations. Our method uses the definition of generalized inverse matrix and the relationship among HSI, MSI, and HR-HSI to approximate the function module of PSF. Then, we calculate the generalized inverse matrix of the spectral response function (SRF). Finally, we optimize the generalized inverse matrix of SRF using the Sylvester equation and multiply it with MSI to obtain HR-HSI. This is the first proposed method to achieve hyperspectral image super-resolution by optimizing the inverse matrix of the spectral response function. Experiments on multiple simulated datasets and one real dataset have validated that our GIMO method outperforms existing state-of-the-art methods, especially regarding fusion time and robustness for various complex scenarios.

Data-Driven Analysis of High-Resolution Hyperspectral Image Data Sets through Nanoscale Capacitance–Voltage Measurements to Visualize Ferroelectric Domain Dynamics

Data-Driven Analysis of High-Resolution Hyperspectral Image Data Sets through Nanoscale Capacitance–Voltage Measurements to Visualize Ferroelectric Domain Dynamics

A hyperspectral surgical microscope with super-resolution reconstruction for intraoperative image guidance.

Hyperspectral imaging (HSI) is an emerging imaging modality in medical applications, especially for intraoperative image guidance. A surgical microscope improves surgeons' visualization with fine details during surgery. The combination of HSI and surgical microscope can provide a powerful tool for surgical guidance. However, to acquire high-resolution hyperspectral images, the long integration time and large image file size can be a burden for intraoperative applications. Super-resolution reconstruction allows acquisition of low-resolution hyperspectral images and generates high-resolution HSI. In this work, we developed a hyperspectral surgical microscope and employed our unsupervised super-resolution neural network, which generated high-resolution hyperspectral images with fine textures and spectral characteristics of tissues. The proposed method can reduce the acquisition time and save storage space taken up by hyperspectral images without compromising image quality, which will facilitate the adaptation of hyperspectral imaging technology in intraoperative image guidance.

Deep learning-enhanced snapshot hyperspectral confocal microscopy imaging system

Laser-scanning confocal hyperspectral microscopy is a powerful technique to identify the different sample constituents and their spatial distribution in three-dimensional (3D). However, it suffers from low imaging speed because of the mechanical scanning methods. To overcome this challenge, we propose a snapshot hyperspectral confocal microscopy imaging system (SHCMS). It combined coded illumination microscopy based on a digital micromirror device (DMD) with a snapshot hyperspectral confocal neural network (SHCNet) to realize single-shot confocal hyperspectral imaging. With SHCMS, high-contrast 160-bands confocal hyperspectral images of potato tuber autofluorescence can be collected by only single-shot, which is almost 5 times improvement in the number of spectral channels than previously reported methods. Moreover, our approach can efficiently record hyperspectral volumetric imaging due to the optical sectioning capability. This fast high-resolution hyperspectral imaging method may pave the way for real-time highly multiplexed biological imaging.

Dual-camera laparoscopic imaging with super-resolution reconstruction for intraoperative hyperspectral image guidance.

Laparoscopic and robotic surgery, as one type of minimally invasive surgery (MIS), has gained popularity due to the improved surgeon ergonomics, instrument precision, operative time, and postoperative recovery. Hyperspectral imaging (HSI) is an emerging medical imaging modality, which has proved useful for intraoperative image guidance. Snapshot hyperspectral cameras are ideal for intraoperative laparoscopic imaging because of their compact size and light weight, but low spatial resolution can be a limitation. In this work, we developed a dual-camera laparoscopic imaging system that consists of a high-resolution color camera and a snapshot hyperspectral camera, and we employed super-resolution reconstruction to fuse the images from both cameras to generate high-resolution hyperspectral images. The experimental results show that our method can significantly improve the resolution of hyperspectral images without compromising the image quality or spectral signatures. The proposed super-resolution reconstruction method is promising to promote the employment of high-speed hyperspectral imaging in laparoscopic surgery.

S2CycleDiff: Spatial-Spectral-Bilateral Cycle-Diffusion Framework for Hyperspectral Image Super-resolution

Hyperspectral image super-resolution (HISR) is a technique that can break through the limitation of imaging mechanism to obtain the hyperspectral image (HSI) with high spatial resolution. Although some progress has been achieved by existing methods, most of them directly learn the spatial-spectral joint mapping between the observed images and the target high-resolution HSI (HrHSI), failing to fully reserve the spectral distribution of low-resolution HSI (LrHSI) and the spatial distribution of high-resolution multispectral imagery (HrMSI). To this end, we propose a spatial-spectral-bilateral cycle-diffusion framework (S2CycleDiff) for HISR, which can step-wise generate the HrHSI with high spatial-spectral fidelity by learning the conditional distribution of spatial and spectral super-resolution processes bilaterally. Specifically, a customized conditional cycle-diffusion framework is designed as the backbone to achieve the spatial-spectral-bilateral super-resolution by repeated refinement, wherein the spatial/spectral guided pyramid denoising (SGPD) module seperately takes HrMSI and LrHSI as the guiding factors to achieve the spatial details injection and spectral correction. The outputs of the conditional cycle-diffusion framework are fed into a complementary fusion block to integrate the spatial and spectral details to generate the desired HrHSI. Experiments have been conducted on three widely used datasets to demonstrate the superiority of the proposed method over state-of-the-art HISR methods. The code is available at https://github.com/Jiahuiqu/S2CycleDiff.

A High-Resolution Hyperspectral Imaging System for the Retina.

In this study, we developed an imaging system that can acquire and produce high-resolution hyperspectral images of the retina. Our system combines the view from a high-resolution RGB camera and a snapshot hyperspectral camera together. The method is fast and can be constructed into a compact imaging device. We tested our system by imaging a calibrated color chart, biological tissues ex vivo, and a phantom of the human retina. By using image pansharpening methods, we were able to produce a high-resolution hyperspectral image. The images from the hyperspectral camera alone have a spatial resolution of 0.2 mm/pixel, whereas the pansharpened images have a spatial resolution of 0.1 mm/pixel, a 2x increase in spatial resolution. Our method has the potential to capture images of the retina rapidly. Our method preserves both the spatial and spectral fidelity, as shown by comparing the original hyperspectral images with the pansharpened images. The high-resolution hyperspectral imaging device can have a variety of applications in retina examinations.

Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images.

(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial-spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.

Hyperspectral and multispectral image fusion via residual selective kernel attention-based U-net

ABSTRACT The fusion of low-resolution hyperspectral image (LR-HSI) and high-resolution multispectral image (HR-MSI) is a crucial technology for producing high-resolution hyperspectral images. Most existing image fusion algorithms based on deep learning do not fully utilize the ability of neural network to extract and process multi-scale features, which leads to the problem of difficulty in fully learning features and ambiguity of features. In order to overcome these issues, a residual selective kernel attention-based U-net named RSKAU-net is designed for LR-HSI and HR-MSI fusion. RSKAU-net is constructed by a residual selective kernel module with an attention mechanism and a channel attention block. The residual selective kernel attention-based (RSKA) module is designed to process images of different resolutions, which adaptively extracts multi-scale features and efficiently emphasizes significant features through the attention mechanism. The channel attention (CA) module retains important spectral information by assigning different weights to each channel of LR-HSI. The proposed network can enhance the spatial information of LR-HSI while preserving its spectral information. Meanwhile, it effectively fuses the features from the source image to obtain the HR-HSI with rich details. The experimental results demonstrate that the proposed network has advantages in terms of both visual effect and objective quantitative indices when compared to existing HSI-MSI fusion approaches.

Hyperspectral Image Fusion via a Novel Generalized Tensor Nuclear Norm Regularization.

Recently, low-rank tensor regularization has received more and more attention in hyperspectral and multispectral fusion (HMF). However, these methods often suffer from inflexible low-rank tensor definition and are highly sensitive to the permutation of tensor modes, which hinder their performance. To tackle this problem, we propose a novel generalized tensor nuclear norm (GTNN)-based approach for the HMF. First, we define a novel GTNN by extending the existing third-mode-based tensor nuclear norm (TNN) to arbitrary mode, which conducts the Fourier transform on an arbitrary single mode and then computes the TNN for each mode. In this way, we can not only capture more extensive correlations for the three modes of a tensor, and also omit the adverse effect of permutation of tensor modes. To utilize the correlations among spectral bands, the high-resolution hyperspectral image (HSI) is approximated as low-rank spectral basis multiplication by coefficients, and we estimate the spectral basis by conducting singular-value decomposition (SVD) on HSI. Then, the coefficients are estimated by addressing the proposed GTNN regularized optimization. In specific, to exploit the non-local similarities of the HSI, we first cluster the patches of the coefficient into a 3-D, which contains spatial, spectral, and non-local modes. Since the collected tensor contains the strong non-local spatial-spectral similarities of the HSI, the proposed low-rank tensor regularization is imposed on these collected tensors, which fully model the non-local self-similarities. Fusion experiments on both simulated and real datasets prove the advantages of this approach. The code is available at https://github.com/renweidian/GTNN.

Advancing Hyperspectral and Multispectral Image Fusion: An Information-Aware Transformer-Based Unfolding Network.

In hyperspectral image (HSI) processing, the fusion of the high-resolution multispectral image (HR-MSI) and the low-resolution HSI (LR-HSI) on the same scene, known as MSI-HSI fusion, is a crucial step in obtaining the desired high-resolution HSI (HR-HSI). With the powerful representation ability, convolutional neural network (CNN)-based deep unfolding methods have demonstrated promising performances. However, limited receptive fields of CNN often lead to inaccurate long-range spatial features, and inherent input and output images for each stage in unfolding networks restrict the feature transmission, thus limiting the overall performance. To this end, we propose a novel and efficient information-aware transformer-based unfolding network (ITU-Net) to model the long-range dependencies and transfer more information across the stages. Specifically, we employ a customized transformer block to learn representations from both the spatial and frequency domains as well as avoid the quadratic complexity with respect to the input length. For spatial feature extractions, we develop an information transfer guided linearized attention (ITLA), which transmits high-throughput information between adjacent stages and extracts contextual features along the spatial dimension in linear complexity. Moreover, we introduce frequency domain learning in the feedforward network (FFN) to capture token variations of the image and narrow the frequency gap. Via integrating our proposed transformer blocks with the unfolding framework, our ITU-Net achieves state-of-the-art (SOTA) performance on both synthetic and real hyperspectral datasets.

Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution.

Fusion-based spectral super-resolution aims to yield a high-resolution hyperspectral image (HR-HSI) by integrating the available high-resolution multispectral image (HR-MSI) with the corresponding low-resolution hyperspectral image (LR-HSI). With the prosperity of deep convolutional neural networks, plentiful fusion methods have made breakthroughs in reconstruction performance promotions. Nevertheless, due to inadequate and improper utilization of cross-modality information, the most current state-of-the-art (SOTA) fusion-based methods cannot produce very satisfactory recovery quality and only yield desired results with a small upsampling scale, thus affecting the practical applications. In this article, we propose a novel progressive spatial information-guided deep aggregation convolutional neural network (SIGnet) for enhancing the performance of hyperspectral image (HSI) spectral super-resolution (SSR), which is decorated through several dense residual channel affinity learning (DRCA) blocks cooperating with a spatial-guided propagation (SGP) module as the backbone. Specifically, the DRCA block consists of an encoding part and a decoding part connected by a channel affinity propagation (CAP) module and several cross-layer skip connections. In detail, the CAP module is customized by exploiting the channel affinity matrix to model correlations among channels of the feature maps for aggregating the channel-wise interdependencies of the middle layers, thereby further boosting the reconstruction accuracy. Additionally, to efficiently utilize the two cross-modality information, we developed an innovative SGP module equipped with a simulation of the degradation part and a deformable adaptive fusion part, which is capable of refining the coarse HSI feature maps at pixel-level progressively. Extensive experimental results demonstrate the superiority of our proposed SIGnet over several SOTA fusion-based algorithms.

Unsupervised Deep Tensor Network for Hyperspectral-Multispectral Image Fusion.

Fusing low-resolution (LR) hyperspectral images (HSIs) with high-resolution (HR) multispectral images (MSIs) is a significant technology to enhance the resolution of HSIs. Despite the encouraging results from deep learning (DL) in HSI-MSI fusion, there are still some issues. First, the HSI is a multidimensional signal, and the representability of current DL networks for multidimensional features has not been thoroughly investigated. Second, most DL HSI-MSI fusion networks need HR HSI ground truth for training, but it is often unavailable in reality. In this study, we integrate tensor theory with DL and propose an unsupervised deep tensor network (UDTN) for HSI-MSI fusion. We first propose a tensor filtering layer prototype and further build a coupled tensor filtering module. It jointly represents the LR HSI and HR MSI as several features revealing the principal components of spectral and spatial modes and a sharing code tensor describing the interaction among different modes. Specifically, the features on different modes are represented by the learnable filters of tensor filtering layers, the sharing code tensor is learned by a projection module, in which a co-attention is proposed to encode the LR HSI and HR MSI and then project them onto the sharing code tensor. The coupled tensor filtering module and projection module are jointly trained from the LR HSI and HR MSI in an unsupervised and end-to-end way. The latent HR HSI is inferred with the sharing code tensor, the features on spatial modes of HR MSIs, and the spectral mode of LR HSIs. Experiments on simulated and real remote-sensing datasets demonstrate the effectiveness of the proposed method.

SSTF-Unet: Spatial-Spectral Transformer-Based U-Net for High-Resolution Hyperspectral Image Acquisition.

To obtain a high-resolution hyperspectral image (HR-HSI), fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) is a prominent approach. Numerous approaches based on convolutional neural networks (CNNs) have been presented for hyperspectral image (HSI) and multispectral image (MSI) fusion. Nevertheless, these CNN-based methods may ignore the global relevant features from the input image due to the geometric limitations of convolutional kernels. To obtain more accurate fusion results, we provide a spatial-spectral transformer-based U-net (SSTF-Unet). Our SSTF-Unet can capture the association between distant features and explore the intrinsic information of images. More specifically, we use the spatial transformer block (SATB) and spectral transformer block (SETB) to calculate the spatial and spectral self-attention, respectively. Then, SATB and SETB are connected in parallel to form the spatial-spectral fusion block (SSFB). Inspired by the U-net architecture, we build up our SSTF-Unet through stacking several SSFBs for multiscale spatial-spectral feature fusion. Experimental results on public HSI datasets demonstrate that the designed SSTF-Unet achieves better performance than other existing HSI and MSI fusion approaches.

CS2DIPs: Unsupervised HSI Super-Resolution Using Coupled Spatial and Spectral DIPs.

In recent years, fusing high spatial resolution multispectral images (HR-MSIs) and low spatial resolution hyperspectral images (LR-HSIs) has become a widely used approach for hyperspectral image super-resolution (HSI-SR). Various unsupervised HSI-SR methods based on deep image prior (DIP) have gained wide popularity thanks to no pre-training requirement. However, DIP-based methods often demonstrate mediocre performance in extracting latent information from the data. To resolve this performance deficiency, we propose a coupled spatial and spectral deep image priors (CS2DIPs) method for the fusion of an HR-MSI and an LR-HSI into an HR-HSI. Specifically, we integrate the nonnegative matrix-vector tensor factorization (NMVTF) into the DIP framework to jointly learn the abundance tensor and spectral feature matrix. The two coupled DIPs are designed to capture essential spatial and spectral features in parallel from the observed HR-MSI and LR-HSI, respectively, which are then used to guide the generation of the abundance tensor and spectral signature matrix for the fusion of the HSI-SR by mode-3 tensor product, meanwhile taking some inherent physical constraints into account. Free from any training data, the proposed CS2DIPs can effectively capture rich spatial and spectral information. As a result, it exhibits much superior performance and convergence speed over most existing DIP-based methods. Extensive experiments are provided to demonstrate its state-of-the-art overall performance including comparison with benchmark peer methods.

Feature Pyramid Fusion Network for Hyperspectral Pansharpening.

Hyperspectral (HS) pansharpening aims at fusing an observed HS image with a panchromatic (PAN) image, to produce an image with the high spectral resolution of the former and the high spatial resolution of the latter. Most of the existing convolutional neural networks (CNNs)-based pansharpening methods reconstruct the desired high-resolution image from the encoded low-resolution (LR) representation. However, the encoded LR representation captures semantic information of the image and is inadequate in reconstructing fine details. How to effectively extract high-resolution and LR representations for high-resolution image reconstruction is the main objective of this article. In this article, we propose a feature pyramid fusion network (FPFNet) for pansharpening, which permits the network to extract multiresolution representations from PAN and HS images in two branches. The PAN branch starts from the high-resolution stream that maintains the spatial resolution of the PAN image and gradually adds LR streams in parallel. The structure of the HS branch remains highly consistent with that of the PAN branch, but starts with the LR stream and gradually adds high-resolution streams. The representations with corresponding resolutions of PAN and HS branches are fused and gradually upsampled in a coarse to fine manner to reconstruct the high-resolution HS image. Experimental results on three datasets demonstrate the significant superiority of the proposed FPFNet over the state-of-the-art methods in terms of both qualitative and quantitative comparisons.

Hyperspectral Image Super Resolution With Real Unaligned RGB Guidance.

Fusion-based hyperspectral image (HSI) super-resolution has become increasingly prevalent for its capability to integrate high-frequency spatial information from the paired high-resolution (HR) RGB reference (Ref-RGB) image. However, most of the existing methods either heavily rely on the accurate alignment between low-resolution (LR) HSIs and RGB images or can only deal with simulated unaligned RGB images generated by rigid geometric transformations, which weakens their effectiveness for real scenes. In this article, we explore the fusion-based HSI super-resolution with real Ref-RGB images that have both rigid and nonrigid misalignments. To properly address the limitations of existing methods for unaligned reference images, we propose an HSI fusion network (HSIFN) with heterogeneous feature extractions, multistage feature alignments, and attentive feature fusion. Specifically, our network first transforms the input HSI and RGB images into two sets of multiscale features with an HSI encoder and an RGB encoder, respectively. The features of Ref-RGB images are then processed by a multistage alignment module to explicitly align the features of Ref-RGB with the LR HSI. Finally, the aligned features of Ref-RGB are further adjusted by an adaptive attention module to focus more on discriminative regions before sending them to the fusion decoder to generate the reconstructed HR HSI. Additionally, we collect a real-world HSI fusion dataset, consisting of paired HSI and unaligned Ref-RGB, to support the evaluation of the proposed model for real scenes. Extensive experiments are conducted on both simulated and our real-world datasets, and it shows that our method obtains a clear improvement over existing single-image and fusion-based super-resolution methods on quantitative assessment as well as visual comparison. The code and dataset are publicly available at https://zeqiang-lai.github.io/HSI-RefSR/.

Sparse Mix-Attention Transformer for Multispectral Image and Hyperspectral Image Fusion

Multispectral image (MSI) and hyperspectral image (HSI) fusion (MHIF) aims to address the challenge of acquiring high-resolution (HR) HSI images. This field combines a low-resolution (LR) HSI with an HR-MSI to reconstruct HR-HSIs. Existing methods directly utilize transformers to perform feature extraction and fusion. Despite the demonstrated success, there exist two limitations: (1) Employing the entire transformer model for feature extraction and fusion fails to fully harness the potential of the transformer in integrating the spectral information of the HSI and spatial information of the MSI. (2) HSIs have a strong spectral correlation and exhibit sparsity in the spatial domain. Existing transformer-based models do not optimize this physical property, which makes their methods prone to spectral distortion. To accomplish these issues, this paper introduces a novel framework for MHIF called a Sparse Mix-Attention Transformer (SMAformer). Specifically, to fully harness the advantages of the transformer architecture, we propose a Spectral Mix-Attention Block (SMAB), which concatenates the keys and values extracted from LR-HSIs and HR-MSIs to create a new multihead attention module. This design facilitates the extraction of detailed long-range information across spatial and spectral dimensions. Additionally, to address the spatial sparsity inherent in HSIs, we incorporated a sparse mechanism within the core of the SMAB called the Sparse Spectral Mix-Attention Block (SSMAB). In the SSMAB, we compute attention maps from queries and keys and select the K highly correlated values as the sparse-attention map. This approach enables us to achieve a sparse representation of spatial information while eliminating spatially disruptive noise. Extensive experiments conducted on three synthetic benchmark datasets, namely CAVE, Harvard, and Pavia Center, demonstrate that the SMAformer method outperforms state-of-the-art methods.

Early Detection of Dicamba and 2,4-D Herbicide Drifting Injuries on Soybean with a New Spatial–Spectral Algorithm Based on LeafSpec, an Accurate Touch-Based Hyperspectral Leaf Scanner

In soybeans, off-target damage from the use of dicamba and 2,4-D herbicides for broadleaf weed control can significantly impact sensitive vegetation and crops. The early detection and assessment of such damage are critical for plant diagnostic labs and regulatory agencies to inform regulated usage policies. However, the existing technologies that calculate the average spectrum often struggle to detect and differentiate the damage caused by these herbicides, as they share a similar mode-of-action. In this study, a high-precision spatial and spectral imaging solution was tested for the early detection of dicamba and 2,4-D-induced damage in soybeans. A 2021 study was conducted using LeafSpec, a touch-based hyperspectral leaf scanner, to detect damage on soybean leaves. VIS-NIR (visible–near infrared) hyperspectral images were captured from 180 soybean plants exposed to nine different herbicide treatments at different intervals after spraying. Leaf damage was distinguished as early as 2 h after treatment (HAT) using pairwise partial least squares discriminant analysis (PLS-DA) models based on spectral data. Leaf color distribution, texture, and morphological features were analyzed to separate herbicide dosages. By fully exploiting the spatial and spectral information from high-resolution hyperspectral images, classification accuracy was improved from 57.4% to over 80% for all evaluation dates. This work demonstrates the potential and advantages of using spectral and spatial features of LeafSpec hyperspectral images for the early and accurate detection of herbicide damage in soybean plants.

Delineating amyloid peptide signatures among heterogenous plaque population in Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease (AD) is the most common neurodegenerative disorder affecting nearly 12% of individuals older than 65 years. The disease poses an immense societal challenge and personal suffering, particularly as there were still no curative treatments as the pathogenic mechanisms of AD remain elusive. One of the main challenge would be to understand the chemical basis of heterogeneity involved in the formation of variety of plaque polymorphs. These plaques are known to be extracellular deposits of amyloid beta (AB) peptide. In this study we aim to investigate the chemical nature of structuraly diverse plaque polymorphs in patient samples with sporadic AD, familial AD and Cognitively unimpaired pathological ageing.MethodWe employed a novel chemical imaging strategy combining microscopy with matrix assisted laser desorption/ioninsation ‐ mass spectrometry imaging (MALDI‐MSI) to delineate AB signatures of heterogenous plaque polymorphs. These plaques are then grouped into sAD‐diffused plaques (DP), sAD‐cored plaques (CP), sAD coarse grain plaques (sAD‐CG), CU‐AP diffused plaques(DP of CU‐AP), fAD cored plaques (fAD‐CP) and fAD coarse grain plaques (fAD‐CG) along with amyloid vasculature from sAD and fAD based on the high resolution hyperspectral microscopy images combined with multivariate image analysis performed on MALDI‐MSI data. These plaques are then investigated for differences in the AB signatures.ResultAβ1‐40 is more abundant in more compact plaques like CG and CP in comparison to DP and DP of CU‐AP. Aβ3pE‐40 in less aggregated diffused plaques of CU‐AP than cored plaques of sAD.ConclusionThis study provides neurochemical information of the amyloid plaque polymorphs crucial for the development of a biomarker . Our data suggests Aβ1‐40 and 3pE depositions are critical in plaque maturation . fAD plaques are more compact than in sAD, larger extend of Aβ1‐40/1‐42. We also present the chemical characterization of coarse grain plaques for the first time. Similarities of CG to CAA point towards vascular involvement in the formation of coarse grain plaques