スペクトルの線形性を考慮したハイパースペクトラル画像のノイズ除去とアンミキシングに関する研究

This study aims to generalize color line to M-dimensional spectral line feature (M>3) and introduce methods for denoising and unmixing of hyperspectral images based on the spectral linearity.For denoising, we propose a local spectral component decomposition method based on the spectral line. We first calculate the spectral line of an M-channel image, then using the line, we decompose the image into three components: a single M-channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, thus the algorithm needs to denoise only two grayscale images, regardless of the number of channels. For unmixing, we propose an algorithm that exploits the low-rank local abundance by applying the unclear norm to the abundance matrix for local regions of spatial and abundance domains. In optimization problem, the local abundance regularizer is collaborated with the L2, 1 norm and the total variation.北九州市立大

Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion

Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing SU methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, the determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this article, a geographically weighted SU (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatiotemporal fusion. SU-GW is a general model suitable for any SU method. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for SU. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatiotemporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatiotemporal fusion, which can be used to update existing methods and future potential versions

Hyperspectral drill-core scanning in geometallurgy

Driven by the need to use mineral resources more sustainably, and the increasing complexity of ore deposits still available for commercial exploitation, the acquisition of quantitative data on mineralogy and microfabric has become an important need in the execution of exploration and geometallurgical test programmes. Hyperspectral drill-core scanning has the potential to be an excellent tool for providing such data in a fast, non- destructive and reproducible manner. However, there is a distinct lack of integrated methodologies to make use of these data through-out the exploration and mining chain. This thesis presents a first framework for the use of hyperspectral drill-core scanning as a pillar in exploration and geometallurgical programmes. This is achieved through the development of methods for (1) the automated mapping of alteration minerals and assemblages, (2) the extraction of quantitative mineralogical data with high resolution over the drill-cores, (3) the evaluation of the suitability of hyperspectral sensors for the pre-concentration of ores and (4) the use of hyperspectral drill- core imaging as a basis for geometallurgical domain definition and the population of these domains with mineralogical and microfabric information.:Introduction Materials and methods Assessment of alteration mineralogy and vein types using hyperspectral data Hyperspectral imaging for quasi-quantitative mineralogical studies Hyperspectral sensors for ore beneficiation 3D integration of hyperspectral data for deposit modelling Concluding remarks Reference

Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation

Spatiotemporal satellite image fusion (STIF) has been widely applied in land surface monitoring to generate high spatial and high temporal reflectance images from satellite sensors. This paper proposed a new unmixing-based spatiotemporal fusion method that is composed of a self-trained random forest machine learning regression (R), low resolution (LR) endmember estimation (E), high resolution (HR) surface reflectance image reconstruction (R), and residual compensation (C), that is, RERC. RERC uses a self-trained random forest to train and predict the relationship between spectra and the corresponding class fractions. This process is flexible without any ancillary training dataset, and does not possess the limitations of linear spectral unmixing, which requires the number of endmembers to be no more than the number of spectral bands. The running time of the random forest regression is about ~1% of the running time of the linear mixture model. In addition, RERC adopts a spectral reflectance residual compensation approach to refine the fused image to make full use of the information from the LR image. RERC was assessed in the fusion of a prediction time MODIS with a Landsat image using two benchmark datasets, and was assessed in fusing images with different numbers of spectral bands by fusing a known time Landsat image (seven bands used) with a known time very-high-resolution PlanetScope image (four spectral bands). RERC was assessed in the fusion of MODIS-Landsat imagery in large areas at the national scale for the Republic of Ireland and France. The code is available at https://www.researchgate.net/proiile/Xiao_Li52

Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 1: AVIRIS Workshop

This publication is the first of three containing summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on January 23-24. The summaries for this workshop appear in this volume; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on January 25-26. The summaries for this workshop appear in Volume 3; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop, on January 26. The summaries for this workshop appear in Volume 2

A New Representation for Spectral Data Applied to Raman Spectroscopy of Brain Cancer

Par sa nature infiltrative et son confinement derrière la barrière hémo-encéphalique, le cancer primaire du cerveau est l’une des néoplasies les plus difficiles à diagnostiquer et traiter. Son traitement repose sur la résection chirurgicale maximale. La spectroscopie Raman, capable d’identifier en temps réel des régions cancéreuses qui apparaîtraient normales à l’œil nu, promet d’améliorer considérablement le guidage neurochirurgical et maximiser la résection de la masse tumorale. Cependant, le signal Raman est très complexe à interpréter : les systèmes Raman peuvent maintenant capter des signaux de grande qualité que les méthodes analytiques actuelles ne parviennent pas à interpréter de manière reproductible. Ceci constitue une barrière importante à l’acceptation de la spectroscopie Raman par les médecins et les chercheurs œuvrant sur le cancer du cerveau. L’objectif de ce travail est de développer une méthode robuste d’ingénierie des variables (« Feature engineering ») qui permettrait d’identifier les processus moléculaires exploités par les systèmes Raman pour différentier les régions cancéreuses des régions saines lors de chirurgies cérébrales. Tout d’abord, nous avons identifié les régions Raman ayant une haute spécificité à notre problématique clinique par une revue systématique de la littérature. Un algorithme d’ajustement de courbe a été développé afin d’extraire la forme des pics Raman dans les régions sélectionnées. Puis, nous avons élaboré un modèle mathématique qui tient compte de l’interactivité entre les molécules de l’échantillon interrogé, ainsi qu’entre le signal Raman et l’âge du patient opéré. Pour valider le modèle, nous avons comparé sa capacité à compresser le signal avec celle de l’analyse en composante principale (ACP), le standard en spectroscopie Raman. Finalement, nous avons appliqué la méthode d’ingénierie des variables à des spectres Raman acquis en salle d’opération afin d’identifier quels processus moléculaires indiquaient la présence de cancer. Notre méthode a démontré une meilleure rétention d’information que l’ACP. En l’appliquant aux spectres Raman in vivo, les zones denses en cellules malignes démontrent une expression augmentée d’acides nucléiques ainsi que de certaines protéines, notamment le collagène, le tryptophan et la phénylalanine. De plus, l’âge des patients semble affecter l’impact qu’ont certaines protéines, lipides et acides nucléiques sur le spectre Raman. Nos travaux révèlent l’importance d’une modélisation statistique appropriée pour l’implémentation clinique de systèmes Raman chirurgicaux.----------ABSTRACT Because of its infiltrative nature and concealment behind the blood-brain barrier, primary brain cancer remains one of the most challenging oncological condition to diagnose and treat. The mainstay of treatment is maximal surgical resection. Raman spectroscopy has shown great promise to guide surgeons intraoperatively by identifying, in real-time, dense cancer regions that appear normal to the naked eye. The Raman signal of living tissue is, however, very challenging to interpret, and while most advances in Raman systems targeted the hardware, appropriate statistical modeling techniques are lacking. As a result, there is conflicting evidence as to which molecular processes are captured by Raman probes. This limitation hinders clinical translation and usage of the technology by the cancer-research community. This work focuses on the analytical aspect of Raman-based surgical systems. Its objective is to develop a robust data processing pipeline to confidently identify which molecular phenomena allow Raman systems to differentiate healthy brain and cancer during neurosurgeries. We first selected high-yield Raman regions based on previous literature on the subject, resulting in a list of reproducible Raman bands with high likelihood of brain-specific Raman signal. We then developed a peak-fitting algorithm to extract the shape (height and width) of the Raman signal at those specific bands. We described a mathematical model that accounted for all possible interactions between the selected Raman peaks, and the interaction between the peaks’ shape and the patient’s age. To validate the model, we compared its capacity to compress the signal while maintaining high information content against a Principal Component Analysis (PCA) of the Raman spectra, the fields’ standard. As a final step, we applied the feature engineering model to a dataset of intraoperative human Raman spectra to identify which molecular processes were indicative of brain cancer. Our method showed better information retention than PCA. Our analysis of in vivo Raman measurement showed that areas with high-density of malignant cells had increased expression of nucleic acids and protein compounds, notably collagen, tryptophan and phenylalanine. Patient age seemed to affect the impact of nucleic acids, proteins and lipids on the Raman spectra. Our work demonstrates the importance of appropriate statistical modeling in the implementation of Raman-based surgical devices

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Assessing hemlock woolly adelgid induced decline and susceptibility using hyperspectral technologies

The ultimate goal of this study was to provide the scientific framework for using narrow band hyperspectral instruments to assess early hemlock decline and susceptibility to the introduced hemlock woolly adelgid (HWA). To this end, spectral data from an ASD FieldSpec Pro was used to develop a 6-term linear regression equation, which predicted a detailed decline rating (0--10) with an R2 of 0.71 and RMSE of 0.591. To scale up this method to a remote sensing platform, NASA\u27s Airborne Visible Infrared Imaging Spectrometer (AVIRIS) was used to create a hemlock abundance map, correctly identifying hemlock dominated pixels (\u3e40% basal area) with 88% accuracy. Reflectance at a chlorophyll sensitive wavelength (683nm), coupled with a water band index (R970/900), was able to predict decline with 85% accuracy. The extreme accuracy at the low (0--4) end of the range indicated that these wavelengths might be used to assess early decline, before visual symptoms are apparent. Because instruments like AVIRIS have the capability to map foliar chemistry, the identification of links between HWA dynamics and foliar chemistry may be used to map relative susceptibility. To this end, we employed a three-tiered approach examining resistant vs. susceptible hemlock species, foliar chemistry vs. colonization success and regional foliar chemistry vs. HWA population levels. We found that HWA resistant hemlock species demonstrated higher concentrations of Ca and P, and lower concentrations of N and K. Regardless of host species, successful colonization of uninfested hemlocks was associated with higher N, and lower Ca and P concentrations. Regionally, higher concentrations of Ca, Mn, N and P were correlated with higher HWA densities. We hypothesize that higher N and K concentrations may have a palatability effect, driving HWA population levels, while higher concentrations of Ca and P may act as deterrents to more severe infestations. These results indicate that by using hyperspectral remote sensing instruments, it is possible to identify the very early stages of hemlock decline and map relative susceptibility to HWA on a landscape scale. Such tools are instrumental in targeting management activities and ultimately controlling the HWA outbreak