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1. Multilayer graph spectral analysis for hyperspectral images

   https://doi.org/10.1186/s13634-022-00926-8  

   Zhang, Songyang; Deng, Qinwen; Ding, Zhi (December 2022, EURASIP Journal on Advances in Signal Processing)

   Abstract Hyperspectral imaging has broad applications and impacts in areas including environmental science, weather, and geo/space exploration. The intrinsic spectral–spatial structures and potential multi-level features in different frequency bands make multilayer graph an intuitive model for hyperspectral images (HSI). To study the underlying characteristics of HSI and to take the advantage of graph signal processing (GSP) tools, this work proposes a multilayer graph spectral analysis for hyperspectral images based on multilayer graph signal processing (M-GSP). More specifically, we present multilayer graph (MLG) models and tensor representations for HSI. By exploring multilayer graph spectral space, we develop MLG-based methods for HSI applications, including unsupervised segmentation and supervised classification. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral–spatial information extraction. 

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2. Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics

   https://doi.org/10.3390/rs13050955  

   Zhang, Shukun; Murphy, James M. (March 2021, Remote Sensing)

   null (Ed.)

   We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and spectral-spatial geometry to distinguish between material classes in the data, without the need for training labels. The proposed method is efficient, with quasilinear scaling in the number of data points, and enjoys robust theoretical performance guarantees. Extensive experiments on synthetic and real HSI data demonstrate its strong performance compared to benchmark and state-of-the-art methods. Indeed, the proposed method not only achieves excellent labeling accuracy, but also efficiently estimates the number of clusters. Thus, unlike almost all existing hyperspectral clustering methods, the proposed algorithm is essentially parameter-free. 

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3. Boosting With Multiple Clustering Memberships For Hyperspectral Image Classification

   https://doi.org/10.1109/SoutheastCon51012.2023.10115209  

   Bellio, Giovanni; Russell, Randy; Kursun, Olcay (April 2023, SoutheastCon 2023)

   A novel hyperspectral image classification algorithm is proposed and demonstrated on benchmark hyperspectral images. We also introduce a hyperspectral sky imaging dataset that we are collecting for detecting the amount and type of cloudiness. The algorithm designed to be applied to such systems could improve the spatial and temporal resolution of cloud information vital to understanding Earth’s climate. We discuss the nature of our HSI-Cloud dataset being collected and an algorithm we propose for processing the dataset using a categorical-boosting method. The proposed method utilizes multiple clusterings to augment the dataset and achieves higher pixel classification accuracy. Creating categorical features via clustering enriches the data representation and improves boosting ensembles. For the experimental datasets used in this paper, gradient boosting methods performed favorably to the benchmark algorithms. 

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4. Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

   https://doi.org/10.3390/rs15041053  

   Polk, Sam L.; Cui, Kangning; Chan, Aland H.; Coomes, David A.; Plemmons, Robert J.; Murphy, James M. (February 2023, Remote Sensing)

   Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels corresponding to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets. 

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5. Quaternion based neural network for hyperspectral image classification

   https://doi.org/10.1117/12.2558808  

   Rao, Shishir Paramathma; Panetta, Karen; Agaian, Sos S. (April 2020, Mobile Multimedia/Image Processing, Security, and Applications 2020)

   Agaian, Sos S.; Jassim, Sabah A.; DelMarco, Stephen P.; Asari, Vijayan K. (Ed.)

   Neural networks have emerged to be the most appropriate method for tackling the classification problem for hyperspectral images (HIS). Convolutional neural networks (CNNs), being the current state-of-art for various classification tasks, have some limitations in the context of HSI. These CNN models are very susceptible to overfitting because of 1) lack of availability of training samples, 2) large number of parameters to fine-tune. Furthermore, the learning rates used by CNN must be small to avoid vanishing gradients, and thus the gradient descent takes small steps to converge and slows down the model runtime. To overcome these drawbacks, a novel quaternion based hyperspectral image classification network (QHIC Net) is proposed in this paper. The QHIC Net can model both the local dependencies between the spectral channels of a single-pixel and the global structural relationship describing the edges or shapes formed by a group of pixels, making it suitable for HSI datasets that are small and diverse. Experimental results on three HSI datasets demonstrate that the QHIC Net performs on par with the traditional CNN based methods for HSI Classification with a far fewer number of parameters. Keywords: Classification, deep learning, hyperspectral imaging, spectral-spatial feature learning 

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