Relevant bibliographies by topics / Hyper spectral image

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  2. Dissertations / Theses
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  4. Conference papers
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Academic literature on the topic 'Hyper spectral image'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Hyper spectral image"

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Sucharitha, B., and Dr K. Anitha Sheela. "Compression of Hyper Spectral Images using Tensor Decomposition Methods." International Journal of Circuits, Systems and Signal Processing 16 (October 7, 2022): 1148–55. http://dx.doi.org/10.46300/9106.2022.16.138.

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Tensor decomposition methods have beenrecently identified as an effective approach for compressing high-dimensional data. Tensors have a wide range of applications in numerical linear algebra, chemo metrics, data mining, signal processing, statics, and data mining and machine learning. Due to the huge amount of information that the hyper spectral images carry, they require more memory to store, process and send. We need to compress the hyper spectral images in order to reduce storage and processing costs. Tensor decomposition techniques can be used to compress the hyper spectral data. The primary objective of this work is to utilize tensor decomposition methods to compress the hyper spectral images. This paper explores three types of tensor decompositions: Tucker Decomposition (TD_ALS), CANDECOMP/PARAFAC (CP) and Tucker_HOSVD (Higher order singular value Decomposition) and comparison of these methods experimented on two real hyper spectral images: the Salinas image (512 x 217 x 224) and Indian Pines corrected (145 x 145 x 200). The PSNR and SSIM are used to evaluate how well these techniques work. When compared to the iterative approximation methods employed in the CP and Tucker_ALS methods, the Tucker_HOSVD method decomposes the hyper spectral image into core and component matrices more quickly. According to experimental analysis, Tucker HOSVD's reconstruction of the image preserves image quality while having a higher compression ratio than the other two techniques.
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Pierau, Ruth-Emely, Alaster Meehan, Hamid Rezatofighi, and Peter J. Stuckey. "Acoustic-to-Hyper-Spectral: Hyper-Spectral Image Construction from Frequency Spectrums Through Simulated Annealing (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29466–68. https://doi.org/10.1609/aaai.v39i28.35290.

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This abstract presents a simulated annealing based approach that constructs hyper-spectral images from the frequency spectrums of a distributed acoustic sensing system and iteratively improves them through the training of learnable filters. The aim is to construct an image that represents features of signals from events while repressing noise. Hyper-spectral images are specifically created for downstream computer vision tasks such as object detection. Hyper-spectral images are images with more than three channels that are derived from a frequency spectrum to obtain the spectrum for each image pixel. Simulated annealing is used to train the filters to automatically select frequencies and bin them into frequency bands. Each frequency band is mapped into an image channel. We fully integrate our filtering method with an object detection network so that filters are trained in conjunction with the neural network. The detection model serves as both the measure and the selector. Our simulated annealing approach significantly outperforms current state-of-the-art methods by a margin of 22%. Limitations include a dependency on randomness and excluding parts of the search space prematuraly due to the design of the local moves.
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Pierau, Ruth-Emely. "Hyper-Spectral Image Generation from Frequency Spectrums." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29291–92. https://doi.org/10.1609/aaai.v39i28.35223.

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My thesis primarily focuses on hyper-spectral image generation from frequency spectrums for downstream computer vision tasks. Hyper-spectral images are images with more than three channels commonly created by special hyper-spectral cameras or from frequency spectrums of various sensing applications such as radargrams or distributed acoustic sensing (DAS) systems. The range of frequencies considered in a frequency spectrum is typically too large to map one frequency to one image channel, i.e. we generally consider a frequency spectrum of 2500 Hz. Frequencies need to be binned together in frequency bands where each band forms one image channel. Usually, frequency bands are created either by expert knowledge or trial-and-error. I research how filters can be trained to automatically select frequencies and bin them into frequency bands. My aim is to represent a variety of signal information and decrease noise. Signal representation is optimised for object detection on time-sequenced images with a set number of image channels. The object detection task consists of localising and classifying events in the generated hyper-spectral images. Events are typically types of intrusions, structural changes, or defined actions and structures, e.g. someone climbing a fence. Events and noise often share at least some frequencies and vary between application types.
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Sandip, Kumar, Kapil Parth, and Bhardwaj |. Uday Shankar Acharya |. Charu Gupta Yatika. "Anomaly Detection in Fruits using Hyper Spectral Images." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 394–97. https://doi.org/10.31142/ijtsrd23753.

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One of the biggest problems in hyper spectral image analysis is the wavelength selection because of the immense amount of hypercube data. In this paper, we introduce an approach to find out the optimal wavelength selection in predicting the quality of the fruit. Hyper spectral imaging was built with spectral region of 400nm to 1000nm for fruit defect detection. For image acquisition, we used fluorescent light as the light source. Analysis was performed in visible region, which had spectral from 413nm to 642nm it was done because of the low reflectance spectrum found in fluorescent light sources. The captured image in this experiment demonstrates irregular illumination that means half of the fruit has brighter area. Analysis of the hyper spectral image was done in order to select diverse wavelengths that could possibly be used in multispectral imaging system. Selected wavelengths were used to create a separate image and each image went through thresholding. Experiment shows a multispectral imaging system which is able to detect defects in fruits by selecting most contributing wavelengths from the hyper spectral image. Algorithm presented in this paper could be improved with morphology operations so that we could get the actual size of the defect. Sandip Kumar | Parth Kapil | Yatika Bhardwaj | Uday Shankar Acharya | Charu Gupta "Anomaly Detection in Fruits using Hyper Spectral Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23753.pdf
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Chatoux, Hermine, Noël Richard, and Bruno Mercier. "Colour key-point detection." London Imaging Meeting 2020, no. 1 (2020): 114–18. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-02.

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A lot of image processing tasks require key-point detection. If grey-level approach are numerous, colour and hyper-spectral ones are scarce. In this paper, we propose a generic key-point detection for colour, multi and hyper-spectral images. A new synthetic database is created to compare key-point detection approaches. Our method improves detection when the image complexity increases.
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Wenbin, Wu, Yue Wu, and Jintao Li. "The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*." MATEC Web of Conferences 173 (2018): 03071. http://dx.doi.org/10.1051/matecconf/201817303071.

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In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.
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HUANG Hong, 黄. 鸿., 陈美利 CHEN Mei-li, 段宇乐 DUAN Yu-le, and 石光耀 SHI Guang-yao. "Hyper-spectral image classification using spatial-spectral manifold reconstruction." Optics and Precision Engineering 26, no. 7 (2018): 1827–36. http://dx.doi.org/10.3788/ope.20182607.1827.

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Stepcenkov, Sergej, Thorsten Wilhelm, and Christian Wöhler. "Learning the Link between Albedo and Reflectance: Machine Learning-Based Prediction of Hyperspectral Bands from CTX Images." Remote Sensing 14, no. 14 (2022): 3457. http://dx.doi.org/10.3390/rs14143457.

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The instruments of the Mars Reconnaissance Orbiter (MRO) provide a large quantity and variety of imagining data for investigations of the Martian surface. Among others, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures visible to infrared reflectance across several hundred spectral bands. However, Mars is only partially covered with targeted CRISM at full spectral and spatial resolution. In fact, less than one percent of the Martian surface is imaged in this way. In contrast, the Context Camera (CTX) onboard the MRO delivers images with a higher spatial resolution and the image data cover almost the entire Martian surface. In this work, we examine to what extent machine learning systems can learn the relation between morphology, albedo and spectral composition. To this end, a dataset of 67 CRISM-CTX image pairs is created and different deep neural networks are trained for the pixel-wise prediction of CRISM bands solely based on the albedo information of a CTX image. The trained models enable us to estimate spectral bands across large areas without existing CRISM data and to predict the spectral composition of any CTX image. The predictions are qualitatively similar to the ground-truth spectra and are also able to recover finer grained details, such as dunes or small craters.
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Lavanya, K., R. Jaya Subalakshmi, T. Tamizharasi, Lydia Jane, and Akila Victor. "Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility." Scalable Computing: Practice and Experience 23, no. 4 (2022): 291–301. http://dx.doi.org/10.12694/scpe.v23i4.2031.

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A crucial component of precision agriculture is the capability to assess the fertility of soil by looking at the precise distribution and composition of its different constituents. This study aims to investigate how different machine learning models may be used to assess soil fertility using hyperspectral pictures. The development of images using a random mixing of different soil components is the first phase, and the hyper spectral bands utilized to create the images are not used again during the analysis procedure. The resulting end members are then acquired by applying the NFINDR algorithm to the process of spectral unmixing this image. The comparison between these end members and the band values of the known elements is then quantified., i.e. it is represented as a graph of band values obtained through spectral unmixing. Finally we quantify the similarities between both graphs and proceed towards the classification of the hyper spectral image as fertile or infertile. In order to classify the hyper spectral image as fertile or infertile, we quantify the similarities between the two graphs. Clustering and picture segmentation algorithms have been devised to help with this process, and a comparison is then made to show which techniques are the most effective.
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Javadi, P. "USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 343–49. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-343-2015.

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The best technique to extract information from remotely sensed image is classification. The problem of traditional classification methods is that each pixel is assigned to a single class by presuming all pixels within the image. Mixed pixel classification or spectral unmixing, is a process that extracts the proportions of the pure components of each mixed pixel. This approach is called spectral unmixing. Hyper spectral images have higher spectral resolution than multispectral images. In this paper, pixel-based classification methods such as the spectral angle mapper, maximum likelihood classification and subpixel classification method (linear spectral unmixing) were implemented on the AVIRIS hyper spectral images. Then, pixel-based and subpixel based classification algorithms were compared. Also, the capabilities and advantages of spectral linear unmixing method were investigated. The spectral unmixing method that implemented here is an effective technique for classifying a hyperspectral image giving the classification accuracy about 89%. The results of classification when applying on the original images are not good because some of the hyperspectral image bands are subject to absorption and they contain only little signal. So it is necessary to prepare the data at the beginning of the process. The bands can be stored according to their variance. In bands with a high variance, we can distinguish the features from each other in a better mode in order to increase the accuracy of classification. Also, applying the MNF transformation on the hyperspectral images increase the individual classes accuracy of pixel based classification methods as well as unmixing method about 20 percent and 9 percent respectively.
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Dissertations / Theses on the topic "Hyper spectral image"

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Hoarau, Romain. "Rendu interactif d'image hyper spectrale par illumination globale pour la prédiction de la signature infrarouge d'aéronefs." Electronic Thesis or Diss., Aix-Marseille, 2019. http://theses.univ-amu.fr.lama.univ-amu.fr/191219_HOARAU_358wfqq893efe918esmfu405fjhqvj_TH.pdf.

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Le dimensionnement de capteur est un enjeu majeur pour le domaine de la détection d'aéronefs. Dans cette optique, il est nécessaire de simuler ces capteurs via des modèles et un nombre conséquent d'images spectrales d'aéronefs. L'obtention de ces images via des campagnes aériennes de mesure est toutefois onéreuse et difficile. Une simulation de ces données s'impose donc. Afin de répondre à ces besoins, des algorithmes d'illumination globale à haute dimension spectrale sont utilisés. Dans ces conditions, ces algorithmes posent des problèmes de consommation mémoire et de temps de calcul. Le projet de recherche de cette thèse s'inscrit dans le cadre de ces problématiques.Dans un premier temps, nous nous sommes focalisés sur l'algorithme du Path Tracing et la parallélisation GPUpour le rendu d'images spectrales. Nous avons d'abord analysé les problèmes de ce type de rendu sur GPU.Nous avons ensuite proposé une nouvelle méthode et un schéma de parallélisation spectral qui permettent de réduire significativement la consommation mémoire et les temps de calcul.Dans un second temps, nous avons cherché à réduire la charge de calcul spectrale de la simulation. À cet égard, nous avons proposé de généraliser le rendu spectral stochastique d'image dans l'espace CIE XYZ en rendu d'image spectrale stochastique. Cette méthode permet de rendre directement et de manière plus précise et rapide les canaux d'un capteur en diminuant la dimension spectrale de la simulation. Pour conclure, les travaux de cette thèse permettent de simuler de manière précise des images multi, hyper et ultra spectrales. Le temps interactif peut être atteint dans notre cas en multi et hyper spectrale<br>Sensor dimensioning is a major issue for the aircraft detection field. In this vein, it is appropriate to simulate these sensorsvia models and a consequent set of spectral images. The acquisition of these images via an airborne measure campaign is unfortunately costly and difficult. A robust and fast simulation of these data is hence very appealing.In order to answer these needs, global illumination methods in high spectral dimension are used. In these circumstances,these methods raise serious issues in term of memory consumption and of computing time. Our research project focuses on these problematics.In the first instance, we have focused on the Path Tracing method and its GPU parallelization for the spectral image rendering. We have investigated at first the issues of this kind of rendering on the GPU. Then we have proposed a new method and an efficient spectral parallelization pattern which allows us to reduce significantly the memory consumption and thecomputing time.In the second phase, we have investigated how to reduce the spectral computational load of the simulation. Inthat sense, we have proposed to generalize the stochastic spectral rendering of color (XYZ) image to the stochastic spectral image rendering. This new method renders directly the channels of a sensor which allows us to reduce the memory andthe computing requirements by reducing the spectral computational load of the simulation.To sum up, the works of this thesis allows us to simulate accurately multi, hyper and ultra spectral images. The interactive time can be achieved in our case in multi and hyper spectral resolution
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Wondim, Yonas kassaw. "Hyperspectral Image Analysis Algorithm for Characterizing Human Tissue." Thesis, Linköpings universitet, Biomedicinsk instrumentteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-75156.

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AbstractIn the field of Biomedical Optics measurement of tissue optical properties, like absorption, scattering, and reduced scattering coefficient, has gained importance for therapeutic and diagnostic applications. Accuracy in determining the optical properties is of vital importance to quantitatively determine chromophores in tissue.There are different techniques used to quantify tissue chromophores. Reflectance spectroscopy is one of the most common methods to rapidly and accurately characterize the blood amount and oxygen saturation in the microcirculation. With a hyper spectral imaging (HSI) device it is possible to capture images with spectral information that depends both on tissue absorption and scattering. To analyze this data software that accounts for both absorption and scattering event needs to be developed.In this thesis work an HSI algorithm, capable of assessing tissue oxygenation while accounting for both tissue absorption and scattering, is developed. The complete imaging system comprises: a light source, a liquid crystal tunable filter (LCTF), a camera lens, a CCD camera, control units and power supply for light source and filter, and a computer.This work also presents a Graphic processing Unit (GPU) implementation of the developed HSI algorithm, which is found computationally demanding. It is found that the GPU implementation outperforms the Matlab “lsqnonneg” function by the order of 5-7X.At the end, the HSI system and the developed algorithm is evaluated in two experiments. In the first experiment the concentration of chromophores is assessed while occluding the finger tip. In the second experiment the skin is provoked by UV light while checking for Erythema development by analyzing the oxyhemoglobin image at different point of time. In this experiment the melanin concentration change is also checked at different point of time from exposure.It is found that the result matches the theory in the time dependent change of oxyhemoglobin and deoxyhemoglobin. However, the result of melanin does not correspond to the theoretically expected result.
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LICCIARDI, GIORGIO ANTONINO. "Neural network architectures for information extraction from hyper-spectral images." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2010. http://hdl.handle.net/2108/1223.

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L’Imaging spectroscopy, meglio conosciuta come telerilevamento da dati iperspettrali, è una tecnica che permette di identificare materiali presenti nell’aria, suolo e acqua, sulla base della riflettanza risultante dall’interazione dell’energia solare con la struttura molecalare dell’elemento. I recenti passi avanti nello sviluppo della sensoristica aeropsaziale ha portato allo sviluppo di strumenti in grado di acquisire centinaia di immagini, rappresentanti intervalli di banda sempre più stretti e contigui, relativi alla stessa zona della superficie terrestre. Come conseguenza di questo, ogni vettore di pixel in un immagine ha associata una “firma spettrale”, che caratterizza univocamente il materiale osservato dal sensore. I sensori iperspettrali ricoprono principalmente lunghezze d’onda che vanno dalla banda del visibile (0.4μm – 0.7 μm) all’infrarosso intermedio (2.4μm). Se consideriamo la consistenza di questo tipo di dati, è facile capire l’ìimportanza di trovare un metodo che permetta di trasformare il dato iniziale composto da centinaia di bande in uno a dimensionalità ridotta ed allo stesso tempo mantenere la maggior quantità di informazione possibile. Queste tecniche sono note come “feature reduction”. Oltre che permettere una gestione migliore del data, le tecniche di feature reduction hanno un ruolo cruciale nell’implementazione di algoritmi di inversione. Questo lavoro cerca di dare un contributo alla ricerca nel campo dell’estrazione dell’informazione dai dati iperspettrali. A questo proposito vengono utilizzati algoritmi di rete neurali, già riconosciuti come una delle migliori famiglie di algoritmi per l’analisi di dati iperspettrali. Oltre alla presentazione di un nuovo approccio per la riduzione della dimensionalità del dato iperspettrale, vengono affrontati anche altri argomenti riguardanti I dati iperspettrali, con particolare attenzione al problema dell’ “unmixing”, meglio conosciuta come classificazione sub-pixel. In questa tesi i primi tre capitoli sono dedicati alla presentazione dei vari problemi, alla descrizione dell’attuale stato dell’arte e alle soluzioni proposte. I capitoli rimanenti vengono invece dedicati alla descrizione e alla valutazione dei risultati ottenuti con diversi scenari applicative. Infine alcune considerazioni concludono il lavoro.<br>Imaging spectroscopy, also known as hyper-spectral remote sensing, is an imaging technique capable of identifying materials and objects in the air, land and water on the basis of the unique reflectance patterns that result from the interaction of solar energy with the molecular structure of the material. Recent advances in aerospace sensor technology have led to the development of instruments capable of collecting hundreds of images, with each image corresponding to narrow contiguous wavelength intervals, for the same area on the surface of the Earth. As a result, each pixel (vector) in the scene has an associated spectral signature or “fingerprint" that uniquely characterizes the underlying objects. Hyper-spectral sensors mainly cover wavelengths from the visible range (0.4_m- 0.7_m) to the middle infrared range (2.4_m). If we consider the consistency of this data, we can easily understand the importance of finding a method which can transform the data cube into one with reduced dimensionality and maintain, at the same time, as much information content as possible. These techniques are known under the general name of feature reduction. Besides enabling an easier storage and management of the data, features reduction procedures can be crucial for the implementation of optimum inversion algorithms. This research work strives to give a contribution along the direction of extracting information from hyperspectral data. A major instrument is considered for this purpose, which is the use of neural networks algorithms, already recognized to represent a rather competitive family of algorithms for the analysis of hyperspectral data. Besides introducing a novel neural network approach for handling the dimensionality reduction of hyperspectral data, other specific issues will be considered, with a special focus on the unmixing problem, or sub-pixel classification. While the first three chapters are dedicated to the presentation of the problems, to the current state of art and to the, theoretically sound, proposed solutions, the remaining sections are dedicated to the description and the assessment of the results obtained in different applicative scenarios. Some final considerations conclude the work.
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Girard, Robin. "Réduction de dimension en statistique et application en imagerie hyper-spectrale." Phd thesis, Grenoble 1, 2008. http://www.theses.fr/2008GRE10074.

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Cette thèse est consacrée à l'analyse statistique de données en grande dimension. Nous nous intéressons à trois problèmes statistiques motivés par des applications médicales : la classification supervisée de courbes, la segmentation supervisée d'images hyperspectrales et la segmentation non-supervisée d'images hyperspectrales. Les procédures développées reposent pour la plupart sur la théorie des tests d'hypothèses (tests multiples, minimax, robustes et fonctionnels) et la théorie de l'apprentissage statistique. Ces théories sont introduites dans une première partie. Nous nous intéressons, dans la deuxième partie, à la classification supervisée de données gaussiennes en grande dimension. Nous proposons une procédure de classification qui repose sur une méthode de réduction de dimension et justifions cette procédure sur le plan pratique et théorique. Dans la troisième et dernière partie, nous étudions le problème de segmentation d'images hyper-spectrales. D'une part, nous proposons un algorithme de segmentation supervisée reposant à la fois sur une analyse multi-échelle, une estimation par maximum de vraisemblance pénalisée, et une procédure de réduction de dimension. Nous justifions cet algorithme par des résultats théoriques et des applications pratiques. D'autre part, nous proposons un algorithme de segmentation non supervisée impliquant une décomposition en ondelette des spectres observées en chaque pixel, un lissage spatial par croissance adaptative de régions et une extraction des frontières par une méthode de vote majoritaire<br>This thesis deals with high dimensional statistical analysis. We focus on three different problems motivated by medical applications : curve classification, pixel classification and clustering in hyperspectral images. Our approaches are deeply linked with statistical testing procedures (multiple testing, minimax testing, robust testing, and functional testing) and learning theory. Both are introduced in the first part of this thesis. The second part focuses on classification of High dimensional Gaussian data. Our approach is based on a dimensionality reduction, and we show practical and theorical results. In the third and last part of this thesis we focus on hyperspectral image segmentation. We first propose a pixel classification algorithm based on multi-scale analysis, penalised maximum likelihood and feature selection. We give theorical results and simulations for this algorithm. We then propose a pixel clustering algorithm. It involves wavelet decomposition of observations in each pixel, smoothing with a growing region algorithm and frontier extraction based on a voting scheme
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Girard, Robin. "Réduction de dimension en statistique et application en imagerie hyper-spectrale." Phd thesis, Université Joseph Fourier (Grenoble), 2008. http://tel.archives-ouvertes.fr/tel-00379179.

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Cette thèse est consacrée à l'analyse statistique de données en grande dimension. Nous nous intéressons à trois problèmes statistiques motivés par des applications médicales : la classification supervisée de courbes, la segmentation supervisée d'images hyperspectrales et la segmentation non-supervisée d'images hyperspectrales. Les procédures développées reposent pour la plupart sur la théorie des tests d'hypothèses (tests multiples, minimax, robustes et fonctionnels) et la théorie de l'apprentissage statistique. Ces théories sont introduites dans une première partie. Nous nous intéressons, dans la deuxième partie, à la classification supervisée de données gaussiennes en grande dimension. Nous proposons une procédure de classification qui repose sur une méthode de réduction de dimension et justifions cette procédure sur le plan pratique et théorique. Dans la troisième et dernière partie, nous étudions le problème de segmentation d'images hyper-spectrales. D'une part, nous proposons un algorithme de segmentation supervisée reposant à la fois sur une analyse multi-échelle, une estimation par maximum de vraisemblance pénalisée, et une procédure de réduction de dimension. Nous justifions cet algorithme par des résultats théoriques et des applications pratiques. D'autre part, nous proposons un algorithme de segmentation non supervisée impliquant une décomposition en ondelette des spectres observées en chaque pixel, un lissage spatial par croissance adaptative de régions et une extraction des frontières par une méthode de vote majoritaire.
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Benhalouche, Fatima Zohra. "Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30083/document.

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Au cours de cette thèse, nous nous sommes intéressés à deux principales problématiques de la télédétection spatiale de milieux urbains qui sont : le "démélange spectral " et la "fusion". Dans la première partie de la thèse, nous avons étudié le démélange spectral d'images hyperspectrales de scènes de milieux urbains. Les méthodes développées ont pour objectif d'extraire, d'une manière non-supervisée, les spectres des matériaux présents dans la scène imagée. Le plus souvent, les méthodes de démélange spectral (méthodes dites de séparation aveugle de sources) sont basées sur le modèle de mélange linéaire. Cependant, lorsque nous sommes en présence de paysage non-plat, comme c'est le cas en milieu urbain, le modèle de mélange linéaire n'est plus valide et doit être remplacé par un modèle de mélange non-linéaire. Ce modèle non-linéaire peut être réduit à un modèle de mélange linéaire-quadratique/bilinéaire. Les méthodes de démélange spectral proposées sont basées sur la factorisation matricielle avec contrainte de non-négativité, et elles sont conçues pour le cas particulier de scènes urbaines. Les méthodes proposées donnent généralement de meilleures performances que les méthodes testées de la littérature. La seconde partie de cette thèse à été consacrée à la mise en place de méthodes qui permettent la fusion des images multispectrale et hyperspectrale, afin d'améliorer la résolution spatiale de l'image hyperspectrale. Cette fusion consiste à combiner la résolution spatiale élevée des images multispectrales et la haute résolution spectrale des images hyperspectrales. Les méthodes mises en place sont des méthodes conçues pour le cas particulier de fusion de données de télédétection de milieux urbains. Ces méthodes sont basées sur des techniques de démélange spectral linéaire-quadratique et utilisent la factorisation en matrices non-négatives. Les résultats obtenus montrent que les méthodes développées donnent globalement des performances satisfaisantes pour la fusion des données hyperspectrale et multispectrale. Ils prouvent également que ces méthodes surpassent significativement les approches testées de la littérature<br>In this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches
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Carter, John. "Étude des minéraux hydratés à la surface de Mars par les imageurs hyperspectraux OMEGA/MEx et CRISM/MRO." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00657804.

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La planète Mars a connu une période où l'eau liquide était durablement stable. Outre les vestiges morphologiques d'une activité hydrologique en surface, l'interaction chimique de l'eau avec la croûte basaltique s'est traduite par la formation d'argiles et de sels hydratés en surface et en profondeur. Ces minéraux hydratés ont été détectés à la surface de Mars en 2004 grâce à l'instrument OMEGA, l'imageur hyperspectral infrarouge proche embarqué sur la sonde européenne Mars Express. Leur étude permet de reconstruire l'histoire de l'activité aqueuse sur Mars et de caractériser une période ancienne où l'environnement a pu être favorable à l'apparition d'une chimie pré-biotique. Ce travail de thèse s'intéresse aux environnements aqueux de Mars en couplant les données minéralogiques des imageurs hyperspectraux embarqués OMEGA/Mars Express et CRISM/Mars Reconnaissance Orbiter avec la morphologie. De nouveaux outils de traitement et d'analyse des données sont développés et ont permis la détection et la caractérisation spectrale de plus d'un millier de dépôts de minéraux hydratés sur Mars, fournissant une vue d'ensemble de l'altération. Celle-ci a eu lieu principalement dans la première partie du Noachien et a surtout formé des phyllosilicates ferro-magnésiens de la famille des vermiculites et smectites. Une importante diversité minérale est par ailleurs constatée avec une dizaine de familles minérales différentes, traçants des conditions géo-chimiques variées. Placés dans leur contexte géologique, certaines détections permettent de proposer l'existence passée d'un système hydrologique sur l'ensemble de la planète qui a donné naissance à un cycle des argiles similaire au cycle terrestre. Il apparait par ailleurs que les cratères d'impact sont le contexte privilégié des minéraux hydratés, mais le lien entre ces derniers et les processus d'impact demeure ambigu. La découverte d'un cycle des argiles est compatible avec l'hypothèse d'une planète potentiellement habitable au Noachien mais qui devra être vérifiée par l'exploration in-situ.
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Lu, Liang-You, and 盧亮有. "Application of FABEMD to hyper-spectral image classification." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/43229087812306432050.

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碩士<br>國立中興大學<br>土木工程學系所<br>101<br>Hyperspectral images provide a great number of spectral information and have been broadly applied to image classifications. However, the scattered pixel problem due to atmosphere noises and incomplete classification leading unsatisfactory classification accuracy remains to be solved. A denoising process includes noise detection and deletion. This paper integrates Fast and Adaptive Bi-dimensional Emperical Mode Decomposition (FABEMD) and Minimum Noise Fraction (MNF) as a two-step denoising process to improve classification accuracy on a hyperspectral image. Regarded as low pass filter, FABEMD decomposes a hyperspectral image into several Bi-dimensional Intrinsic Mode Functions (BIMFs) and a residue image. Some of BIMF are integrated through image fusion to extracted informative images which is subsequently classified through a SVM classifier. The proposed two-step denoising process was tested on AVIRIS Indian Pines hyperspectral image and enhanced the overall accuracy up to 98.14% on the 16-classes classification. The result obtains a significant improvement in hyperspectral classification accuracy compared to the traditional and MNF-based SVMs. The proposed two-step denoising process combining FABEMD with MNF was proven to effectively eliminate a noise effect on hyperspectral images.
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He, Yuan. "Hyper-spectral image processing using high performance reconfigurable computers." 2004. http://etd.utk.edu/2004/HeYuan.pdf.

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Thesis (M.S.)--University of Tennessee, Knoxville, 2004.<br>Title from title page screen (viewed May 17, 2004). Thesis advisor: Gregory Peterson. Document formatted into pages (x, 123 p. : col. ill.). Vita. Includes bibliographical references (p. 74-77).
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THAPLIYAL, ANKITA. "CONVOLUTIONAL NETWORK FEATURE HIERARCHY FOR HYPER SPECTRAL IMAGE CLASSIFICATION." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20109.

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Hyper spectral image classification is the recent technology that is famous among the researchers nowadays. It is simply an application of remote sensing methodology. The results of remote sensing are basically needs to get studied by the scientists properly so that they can analyze the surface area and the target information accordingly. Then next to the study of target information further conclusions can be made about that area successfully. Remote sensing is the first step to analyze the whole process in which the satellite helps to provide several images of a particular land area or vegetation portion. These images can be obtained by using active or passive remote sensing depending on the choice of user. As soon as the images are received by the sensors we not only analyse them in visible spectrum, but we do recognize them in ultra violet and infrared region of the electromagnetic spectrum. This type of technique is known as hyper spectral imaging. We use hyper spectral sensors to perform this type of imaging. This method has so many advantages over multispectral imaging in which number of spectral band information is comparatively less. Since the number of bands in hyper spectral imaging is greater than the band information in multi spectral imaging, the recognition of images and target is more specified and accurate for hyper spectral data. More significant information is obtained through hyper spectral imaging. Since we receive the data through hyper spectral imaging we need to apply the upcoming tasks to know the target area in deeper way. As soon as the input is received in the form of images that are now three dimensional due to the hyper spectral view, we need to classify these images into the categories they are having. For instance, we get the information of a vegetation area we need to classify this three dimensional image data into the different categories of vegetation in that particular portion of land. This whole process is known as image classification which is the latest topic for machine learning methods. The use of deep neural networks at present helps in doing the classification of large number of images at a time with much more accuracy and reduced complexity. In past few decades many researchers have provided their own supervised models to implement the image classification over a huge dataset to classify the images successfully. But due to the drawbacks like less accuracy and higher complexity, these models have been over take by convolutional neural networks. Supervised technology is a type of machine learning task where the model learn itself on the basis of input and the outputs provided at the time of training. The methods like SVM and CNN are supervised methods that we us for the classification purpose. Hence in this project instead of using multi spectral data, we have discussed the use of hyper spectral data. This chapter consist of six chapters. In chapter 1 we are discussing the basic of remote sensing and its types. Chapter 2 will tell us about the type of imaging method and their advantages and disadvantages so that we can prefer the suitable one to perform the objective. In chapter 3 we are looking over the multiple supervised methods like SVM, CNN and ANN that helps in the classification of hyper spectral image data. Chapter 4 and 5 are the discussion of latest model with increased accuracy and reduced complexity.
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Book chapters on the topic "Hyper spectral image"

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Chilkewar, Vijay, and Vibha Vyas. "Hyper-spectral Image Denoising Using Sparse Representation." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1081-6_34.

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Yu, Yi, Yi-Fan Li, Jun-Bao Li, Jeng-Shyang Pan, and Wei-Min Zheng. "The Election of Spectrum bands in Hyper-spectral image classification." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50212-0_1.

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Priyadharshini @ Manisha, K., and B. Sathya Bama. "Hyper-Spectral Image Classification with Support Vector Machine." In Advances in Automation, Signal Processing, Instrumentation, and Control. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_51.

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Debnath, Tanmoy, Sourabhi Debnath, and Manoranjan Paul. "Detection of Age and Defect of Grapevine Leaves Using Hyper Spectral Imaging." In Image and Video Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34879-3_8.

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Rajadell, Olga, Pedro García-Sevilla, and Filiberto Pla. "On the Influence of Spatial Information for Hyper-spectral Satellite Imaging Characterization." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21257-4_57.

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Mahalakshmi, R., Trapty Agarwal, Jayashree M. Kudari, and Ritika Mehra. "Investigating Time Series Clustering Algorithms for Hyper Spectral Image Analysis." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8043-3_24.

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Vaddi, Radhesyam, and Prabukumar Manoharan. "Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16660-1_84.

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Chi, Tao, Yang Wang, Ming Chen, and Manman Chen. "Hyper-Spectral Image Classification by Multi-layer Deep Convolutional Neural Networks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29516-5_65.

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Kumar, R. M. Sunil, Trapty Agarwal, Deepak Mehta, and Arjun Singh. "Unlocking New Opportunities for Crop Management Through Hyper Spectral Image Analysis." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8043-3_31.

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Yang, Ming-Der, Kai-Siang Huang, Ji-Yuan Lin, and Pei Liu. "Application of Support Vector Machines to Airborne Hyper-Spectral Image Classification." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12990-2_50.

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Conference papers on the topic "Hyper spectral image"

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Soni, Pawan, Pavan C. Shekar, and Vivek Kanhangad. "HyperSpectraNet: Leveraging Spectral Attention for Hyper-Spectral Image Reconstruction." In 2024 International Conference on Signal Processing and Communications (SPCOM). IEEE, 2024. http://dx.doi.org/10.1109/spcom60851.2024.10631616.

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Kumari, Anupam, Swati Gupta, and K. P. Premalatha. "Investigating Fuzzy Time Series for Hyper Spectral Image Recognition." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723960.

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Traisa, Roopa, Kirtimaya Mishra, and Zahid Ahmed. "Exploring the Role of Hyper Spectral Image Analysis for Estimating Soil Quality." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724320.

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Sen, Sumanta, Pawan Bhambu, and R. Satish Kumar. "Remote Monitoring of Nutrient Stress in Agricultural Crops Using Hyper Spectral Image Analysis." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724757.

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Shankar Prasd, S., Nikita Shukla, and Harshita Kaushik. "Employing Hyper Spectral Image Analysis to Assess Wind and Rain Damage to Crops." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725795.

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Santhosha, T., H. Mohammed Ali, Seeniappan K, Nagendar Yamsani, R. Maranan, and S. Kaliappan. "Optimized Dilated Convolutional Neural Network with Quantum Self-Attention Based on Hyper Spectral Remote Sensing Image Classification." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911332.

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Dabhade, Siddharth B., Nagsen S. Bansod, Yogesh S. Rode, M. M. Kazi, and K. V. Kale. "Hyper spectral face image based biometric recognition." In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). IEEE, 2016. http://dx.doi.org/10.1109/icgtspicc.2016.7955363.

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Hoarau, Romain, Eric Coiro, Sébastien Thon, and Romain Raffin. "Interactive Hyper Spectral Image Rendering on GPU." In International Conference on Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006549800710080.

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Dabhade, Siddharth B., Nagsen Bansod, Y. S. Rode, M. M. Kazi, Sumegh Tharewal, and K. V. Kale. "Hyper spectral image analysis for human authentication." In 2017 Fourth International Conference on Image Information Processing (ICIIP). IEEE, 2017. http://dx.doi.org/10.1109/iciip.2017.8313729.

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Kursun, Olcay, Fethullah Karabiber, Cemalettin Koc, and Abdullah Bal. "Hyper-spectral image segmentation using spectral clustering with covariance descriptors." In IS&T/SPIE Electronic Imaging, edited by Jaakko T. Astola, Karen O. Egiazarian, Nasser M. Nasrabadi, and Syed A. Rizvi. SPIE, 2009. http://dx.doi.org/10.1117/12.811132.

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Reports on the topic "Hyper spectral image"

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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Adokwei, Abraham Addo. Yield Prediction from Hyper-Spectral Images. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-1617.

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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.
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Mohan, Anish, Guillermo Sapiro, and Edward Bosch. Spatially-Coherent Non-Linear Dimensionality Reduction and Segmentation of Hyper-Spectral Images (PREPRINT). Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada478496.

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