A physics-driven generative adversarial network (GAN) was established here for single-pixel hyperspectral imaging (HSI) in the infrared spectrum, to eliminate the extensive data training work required by traditional data-driven model. Within the GAN framework, the physical process of single-pixel imaging (SPI) was integrated into the generator, and the actual and estimated one-dimensional (1D) bucket signals were employed as constraints in the objective function to update the network's parameters and optimize the generator with the assistance of the discriminator. In comparison to single-pixel infrared HSI methods based on compressed sensing and physics-driven convolution neural networks, our physics-driven GAN-based single-pixel infrared HSI can achieve higher imaging performance but with fewer measurements. We believe that this physics-driven GAN will promote practical applications of computational imaging, especially various SPI-based techniques.
University of Ontario Institute of Technology（安大略理工大学）；University of Toronto（多伦多大学）
This paper presents a systematic study of the effects of hyperspectral pixel dimensionality reduction on the pixel classification task. We use five dimensionality reduction methods -- PCA, KPCA, ICA, AE, and DAE -- to compress 301-dimensional hyperspectral pixels. Compressed pixels are subsequently used to perform pixel classifications. Pixel classification accuracies together with compression method, compression rates, and reconstruction errors provide a new lens to study the suitability of a compression method for the task of pixel classification. We use three high-resolution hyperspectral i…
Quantum Information-Empowered Graph Neural Network for Hyperspectral Change Detection
National Cheng Kung University（成功大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；LJK（让·昆廷诺实验室）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）
Hyperspectral images, which record the electromagnetic spectrum for a pixel in the image of a scene, often store hundreds of channels per pixel and contain an order of magnitude more information than a typical similarly-sized color image. Consequently, concomitant with the decreasing cost of capturing these images, there is a need to develop efficient techniques for storing, transmitting, and analyzing hyperspectral images. This paper develops a method for hyperspectral image compression using implicit neural representations where a multilayer perceptron network $Φ_θ$ with sinusoidal activatio…
We propose a completely unsupervised pixel-wise anomaly detection method for hyperspectral images. The proposed method consists of three steps called data preparation, reconstruction, and detection. In the data preparation step, we apply a background purification to train the deep network in an unsupervised manner. In the reconstruction step, we propose to use three different deep autoencoding adversarial network (AEAN) models including 1D-AEAN, 2D-AEAN, and 3D-AEAN which are developed for working on spectral, spatial, and joint spectral-spatial domains, respectively. The goal of the AEAN mode…
Lumosaic: Hyperspectral Video via Active Illumination and Coded-Exposure Pixels
Department of Computer Science, University of Toronto, Canada（计算机科学系，多伦多大学）；Department of Electrical & Computer Engineering, University of Toronto, Canada（电气与计算机工程系，多伦多大学）
University of Southern California（南加利福尼亚大学）；Information Sciences Institute（信息科学研究所）
Hyperspectral cameras generate a large amount of data due to the presence of hundreds of spectral bands as opposed to only three channels (red, green, and blue) in traditional cameras. This requires a significant amount of data transmission between the hyperspectral image sensor and a processor used to classify/detect/track the images, frame by frame, expending high energy and causing bandwidth and security bottlenecks. To mitigate this problem, we propose a form of processing-in-pixel (PIP) that leverages advanced CMOS technologies to enable the pixel array to perform a wide range of complex …
We introduce a novel video-rate hyperspectral imager with high spatial, and temporal resolutions. Our key hypothesis is that spectral profiles of pixels in a super-pixel of an oversegmented image tend to be very similar. Hence, a scene-adaptive spatial sampling of an hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions. To achieve this, we acquire an RGB image of the scene, compute its super-pixels, from which we generate a spatial mask of locations where we measure high-resolution spectrum. The hyperspectral image is subsequently…
Hyperspectral image (HSI) classification is the most vibrant area of research in the hyperspectral community due to the rich spectral information contained in HSI can greatly aid in identifying objects of interest. However, inherent non-linearity between materials and the corresponding spectral profiles brings two major challenges in HSI classification: interclass similarity and intraclass variability. Many advanced deep learning methods have attempted to address these issues from the perspective of a region/patch-based approach, instead of a pixel-based alternate. However, the patch-based app…
An efficient spatial regularization method using superpixel segmentation and graph Laplacian regularization is proposed for sparse hyperspectral unmixing method. Since it is likely to find spectrally similar pixels in a homogeneous region, we use a superpixel segmentation algorithm to extract the homogeneous regions by considering the image boundaries. We first extract the homogeneous regions, which are called superpixels, then a weighted graph in each superpixel is constructed by selecting $K$-nearest pixels in each superpixel. Each node in the graph represents the spectrum of a pixel and edg…
Qom University of Technology（库姆理工大学）；Freie Universität Berlin（柏林自由大学）
Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the important areas in remote sensing (RS) that needs to be carefully addressed in different RS applications. Despite the high spectral resolution of the hyperspectral data, the relatively low spatial resolution of the sensors may lead to mixture of different pure materials within the image pixels. In this case, the spectrum of a given pixel recorded by the sensor can be a combination of multiple spectra each belonging to a unique material in that pixel. Spectral unmixing is then used as a technique to extract the spectral charact…
This paper proposes a novel two-stage method for the classification of hyperspectral images. Pixel-wise classifiers, such as the classical support vector machine (SVM), consider spectral information only; therefore they would generate noisy classification results as spatial information is not utilized. Many existing methods, such as morphological profiles, superpixel segmentation, and composite kernels, exploit the spatial information too. In this paper, we propose a two-stage approach to incorporate the spatial information. In the first stage, an SVM is used to estimate the class probability …
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural formulation for this task. This method does not require the presence of pure pixels (i.e., pixels containing a single material) but instead represents endmembers as convex combinations of a few pixels present in the original hyperspectral image. Our approach leverages an entropic gradient descent strategy, which (i) provides better solutions for hyperspectral unmixing than traditional archetypal analysis algorithms, an…
Using light spectra is an essential element in many applications, for example, in material classification. Often this information is acquired by using a hyperspectral camera. Unfortunately, these cameras have some major disadvantages like not being able to record videos. Therefore, multispectral cameras with wide-band filters are used, which are much cheaper and are often able to capture videos. However, using multispectral cameras requires an additional reconstruction step to yield spectral information. Usually, this reconstruction step has to be done in the presence of imaging noise, which d…
Beijing Institute of Technology（北京理工大学）；Space star technology co., LTD（航天恒星科技有限公司）
In hyperspectral, high-quality spectral signals convey subtle spectral differences to distinguish similar materials, thereby providing unique advantage for anomaly detection. Hence fine spectra of anomalous pixels can be effectively screened out from heterogeneous background pixels. Since the same materials have similar characteristics in spatial and spectral dimension, detection performance can be significantly enhanced by jointing spatial and spectral information. In this paper, a spectralspatial fusion anomaly detection (SSFAD) method is proposed for hyperspectral imagery. First, original s…
A Deep Learning Approach for Pixel-level Material Classification via Hyperspectral Imaging
Institute of Communication and Computer Systems, National Technical University of Athens（通信与计算机系统研究所，国家技术大学雅典）；School of Electrical and Computing Engineering, National Technical University of Athens（电气与计算工程学院，国家技术大学雅典）；Technology Innovation Institute, Yas Island, Abu Dhabi,United Arab Emirates（亚斯岛技术创新研究所，阿布扎比，阿拉伯联合酋长国）
Several approaches have been proposed to solve the spectral unmixing problem in hyperspectral image analysis. Among them the use of sparse regression techniques aims to characterize the abundances in pixels based on a large library of spectral signatures known a priori. Recently, the integration of image spatial-contextual information significantly enhanced the performance of sparse unmixing. In this work, we propose a computationally efficient multiscale representation method for hyperspectral data adapted to the unmixing problem. The proposed method is based on a hierarchical extension of th…
A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver
Ruđer Bošković Institute（鲁杰尔·博什科维奇研究所）；Technical University Munich（慕尼黑工业大学）；University of Montreal（蒙特利尔大学）；Photon etc.（Photon etc.公司）；Clinical Hospital Center Zagreb（萨格勒布临床医院中心）；University of Zagreb（萨格勒布大学）；Clinical Hospital Dubrava（杜布拉瓦临床医院）
Hong Kong Baptist University（香港浸会大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University（深圳大学）；Instituto de Telecomunicações（电信研究院）；Instituto Superior Técnico（里斯本高等技术学院）；Universidade de Lisboa（里斯本大学）
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications…
Instituto de Telecomunicações（电信研究院）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）
This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduce…
This paper presents a novel Bayesian approach for hyperspectral image unmixing. The observed pixels are modeled by a linear combination of material signatures weighted by their corresponding abundances. A spike-and-slab abundance prior is adopted to promote sparse mixtures and an Ising prior model is used to capture spatial correlation of the mixture support across pixels. We approximate the posterior distribution of the abundances using the expectation-propagation (EP) method. We show that it can significantly reduce the computational complexity of the unmixing stage and meanwhile provide unc…
Nanjing University of Science and Technology（南京理工大学）；Second Institute of Oceanography, Ministry of Natural Resources（自然资源部第二海洋研究所）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Mississippi State University（密西西比州立大学）；Southern Marine Science and Engineering Guangdong Laboratory（南方海洋科学与工程广东省实验室）
Deep learning based methods, such as Convolution Neural Network (CNN), have demonstrated their efficiency in hyperspectral image (HSI) classification. These methods can automatically learn spectral-spatial discriminative features within local patches. However, for each pixel in an HSI, it is not only related to its nearby pixels but also has connections to pixels far away from itself. Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification. In the proposed fra…
Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；International Research Center of Big Data for Sustainable Development Goals（国际可持续发展大数据研究中心）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；Beijing Institute of Remote Sensing Equipment（北京遥感设备研究所）；Univ. Grenoble Alpes, CNRS, Grenoble INP, IJK（格勒诺布尔大学、法国国家科学研究中心、格勒诺布尔理工学院、图像与通信实验室）
Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapshot HSI cameras enable real-time imaging with significant potential for clinical applications. Despite this, the investigation into the relative performance of HSI over RGB imaging for semantic segmentation purposes has been limited, particularly in the context of medical imaging. …
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程研究中心，计算机学院，武汉大学）；Aerospace Information Research Institute, Henan Academy of Sciences（航天信息研究所，河南科学院）
Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made for developing a variety of methods, most of them rely on supervised strategies. Subspace clustering methods, such as Sparse Subspace Clustering (SSC), have become a popular tool for unsupervised learning due to their high performance. However, the computational complexity of SSC methods prevents their use on large spectral remotely sensed datasets. Furthermore, since SSC ignores the spatial informati…
Beijing University of Chemical Technology（北京化工大学）；Sugon Information Industry Co., Ltd（曙光信息产业有限公司）
The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image, which limits the classification accuracy and universality of their methods. This paper proposed a novel method to take into the information of remote sensing image, i.e., geographic latitude-longitude information. In addition, a dual-branch convolutional neural network (CNN) cl…
Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performance
Department of Electrical and Electronic Engineering, University of Peradeniya（珀斯德尼亚大学电子与电气工程系）；School of Electrical and Computer Engineering, Purdue University（普渡大学电子与计算机工程学院）
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；The University of Tokyo（东京大学）；RIKEN（日本理化学研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perform the fusion task by means of multifarious pixel-level priors. Yet the intrinsic effects of a large distribution gap between HS-MS data due to differences in the spatial and spectral resolution are less investigated. The gap might be caused by unknown sensor-specific properties or highly-mixed spectral information within one pixel (due to low spatial resolution). To this end, we propose a subpixel-level HS super-re…
Monash University（莫纳什大学）；Sri Lanka Technological Campus（斯里兰卡理工学院）；University of Peradeniya（佩拉德尼亚大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances. The endmembers of a hyperspectral image (HSI) are more likely to be generated by independent sources and be mixed in a macroscopic degree before arriving at the sensor element of the imaging spectrometer as mixed spectra. Over the past few decades, many attempts have focused on imposing auxiliary constraints on the conventional nonnegative matrix factorization (NMF) framework in order to effectively u…
Current methods for diagnosing the progression of multiple types of cancer within patients rely on interpreting stained needle biopsies. This process is time-consuming and susceptible to error throughout the paraffinization, Hematoxylin and Eosin (H&E) staining, deparaffinization, and annotation stages. Fourier Transform Infrared (FTIR) imaging has been shown to be a promising alternative to staining for appropriately annotating biopsy cores without the need for deparaffinization or H&E staining with the use of Fourier Transform Infrared (FTIR) images when combined with machine learning to int…
Nanjing University（南京大学）；Beijing Institute of Technology（北京理工大学）
Deep learning methods have been successfully applied to hyperspectral image (HSI) classification with remarkable performance. Because of limited labelled HSI data, earlier studies primarily adopted a patch-based classification framework, which divides images into overlapping patches for training and testing. However, this approach results in redundant computations and possible information leakage. In this study, we propose an objective evaluation-based high-efficiency learning framework for tiny HSI classification. This framework comprises two main parts: (i) a leakage-free balanced sampling s…
Technion Israel Institute of Technology（以色列理工学院）；Bar-Ilan University（巴伊兰大学）
Training a deep neural network for classification constitutes a major problem in remote sensing due to the lack of adequate field data. Acquiring high-resolution ground truth (GT) by human interpretation is both cost-ineffective and inconsistent. We propose, instead, to utilize high-resolution, hyperspectral images for solving this problem, by unmixing these images to obtain reliable GT for training a deep network. Specifically, we simulate GT from high-resolution, hyperspectral FENIX images, and use it for training a convolutional neural network (CNN) for pixel-based classification. We show h…
