Relevant bibliographies by topics / HYPER SPECTRAL IMAGE CLASSIFICATION

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Academic literature on the topic 'HYPER SPECTRAL IMAGE CLASSIFICATION'

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Published: 11 September 2023

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Journal articles on the topic "HYPER SPECTRAL IMAGE CLASSIFICATION"

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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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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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Alhayani, Bilal, and Haci Ilhan. "Hyper spectral Image classification using Dimensionality Reduction Techniques." IJIREEICE 5, no. 4 (April 15, 2017): 71–74. http://dx.doi.org/10.17148/ijireeice.2017.5414.

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Sharif, I., and S. Khare. "Comparative Analysis of Haar and Daubechies Wavelet for Hyper Spectral Image Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 937–41. http://dx.doi.org/10.5194/isprsarchives-xl-8-937-2014.

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With the number of channels in the hundreds instead of in the tens Hyper spectral imagery possesses much richer spectral information than multispectral imagery. The increased dimensionality of such Hyper spectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be useful without dimension reduction pre-processing. So dimension reduction has become a significant part of Hyper spectral image processing. This paper presents a comparative analysis of the efficacy of Haar and Daubechies wavelets for dimensionality reduction in achieving image classification. Spectral data reduction using Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. Daubechies wavelets optimally capture the polynomial trends while Haar wavelet is discontinuous and resembles a step function. The performance of these wavelets are compared in terms of classification accuracy and time complexity. This paper shows that wavelet reduction has more separate classes and yields better or comparable classification accuracy. In the context of the dimensionality reduction algorithm, it is found that the performance of classification of Daubechies wavelets is better as compared to Haar wavelet while Daubechies takes more time compare to Haar wavelet. The experimental results demonstrate the classification system consistently provides over 84% classification accuracy.
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Shanmugapriya, G., and . "An Efficient Spectral Spatial Classification for Hyper Spectral Images." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 1050. http://dx.doi.org/10.14419/ijet.v7i3.12.17630.

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An expanded random walker comprises of two primary advances ghostly spatial order strategy for hyper Ghastly pictures. To begin with go to pixel astute order by utilizing bolster vector machine (SVM) which is arrangement likelihood maps for a hyper unearthly picture. Probabilities of hyper phantom Pixel have a place with various classes. The second approach is getting pixel shrewd likelihood maps are upgraded broadened arbitrary walker calculation. Pixel astute measurements data by SVM classifier, spatial relationship between neighboring pixels displayed through weight of diagram edges preparation and test tests demonstrated irregular walkers. These 3 components utilizing for the class of validating pixel are resolved. So, these three elements considered in ERW. The proposed technique demonstrates great order performs for three generally utilized genuine hyper otherworldly informational collections even the quantity of preparing tests is moderately little.
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Banit', Ibtissam, N. A. ouagua, Mounir Ait Kerroum, Ahmed Hammouch, and Driss Aboutajdine. "Band selection by mutual information for hyper-spectral image classification." International Journal of Advanced Intelligence Paradigms 8, no. 1 (2016): 98. http://dx.doi.org/10.1504/ijaip.2016.074791.

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TANG Yan-hui, 唐艳慧, 赵鹏 ZHAO Peng, and 王承琨 WANG Cheng-kun. "Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra." Chinese Journal of Liquid Crystals and Displays 34, no. 12 (2019): 1182–90. http://dx.doi.org/10.3788/yjyxs20193412.1182.

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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 (December 23, 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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Zhang, Tianxiang, Wenxuan Wang, Jing Wang, Yuanxiu Cai, Zhifang Yang, and Jiangyun Li. "Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification." Remote Sensing 14, no. 20 (October 20, 2022): 5251. http://dx.doi.org/10.3390/rs14205251.

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Hyperspectral sensors provide an opportunity to capture the intensity of high spatial/spectral information and enable applications for high-level earth observation missions, such as accurate land cover mapping and target/object detection. Currently, convolutional neural networks (CNNs) are good at coping with hyperspectral image processing tasks because of the strong spatial and spectral feature extraction ability brought by hierarchical structures, but the convolution operation in CNNs is limited to local feature extraction in both dimensions. In the meanwhile, the introduction of the Transformer structure has provided an opportunity to capture long-distance dependencies between tokens from a global perspective; however, Transformer-based methods have a restricted ability to extract local information because they have no inductive bias, as CNNs do. To make full use of these two methods’ advantages in hyperspectral image processing, a dual-flow architecture named Hyper-LGNet to couple local and global features is firstly proposed by integrating CNN and Transformer branches to deal with HSI spatial-spectral information. In particular, a spatial-spectral feature fusion module (SSFFM) is designed to maximally integrate spectral and spatial information. Three mainstream hyperspectral datasets (Indian Pines, Pavia University and Houston 2013) are utilized to evaluate the proposed method’s performance. Comparative results show that the proposed Hyper-LGNet achieves state-of-the-art performance in comparison with the other nine approaches concerning overall accuracy (OA), average accuracy (AA) and kappa index. Consequently, it is anticipated that, by coupling CNN and Transformer structures, this study can provide novel insights into hyperspectral image analysis.
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Li, Runya, and Shenglian Li. "Multimedia Image Data Analysis Based on KNN Algorithm." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/7963603.

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In order to improve the authenticity of multispectral remote sensing image data analysis, the KNN algorithm and hyperspectral remote sensing technology are used to organically combine advanced multimedia technology with spectral technology to subdivide the spectrum. Different classification methods are used to classify CHRIS 0°, and the results are analyzed and compared: SVM classification accuracy is the highest 72 8448%, Kappa coefficient is 0.6770, and SVM is used to classify CHRIS images from five angles, and the results are compared and analyzed: the classification accuracy is from high to low, and the order is FZA = 0 > FZA = −36 > FZA = −55 > FZA = 36 > FZA = 55; SVM is used to classify the multiangle combined image, and the result is compared with the CHRIS 0° result: the overall classification accuracy of angle-combined image types is lower than that of single-angle images; the SVM is used to classify the band-combined image, and the result is compared with CHRIS 0°: the overall classification accuracy of the band combination image forest type is very low, and the effect is not as good as the combining multiangle image classification results. It is verified that if CHRIS multiangle hyper-spectral data are used for classification, the SVM method should be used to classify spectral remote sensing image data with the best effect.
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Dissertations / Theses on the topic "HYPER SPECTRAL IMAGE CLASSIFICATION"

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Kliman, Douglas Hartley. "Rule-based classification of hyper-temporal, multi-spectral satellite imagery for land-cover mapping and monitoring." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/187473.

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A rule-based classification model was developed to derive land-cover information from a large set of hyper-temporal, multi-spectral satellite imagery encompassing the state of Arizona. The model uses Advanced Very High Resolution Radiometer (AVHRR) imagery and the 30-minute digital elevation model (DEM) from the EROS Data Center (EDC) Conterminous U.S. AVHRR Biweekly Composites. Sixty one images from 1990, 1991 and 1992 were analyzed using the Brown & Lowe (1973) Natural Vegetative Communities of Arizona map to identify temporal patterns of Normalized Difference Vegetation Index (NDVI) and thermal measurements for 13 land-cover classes. Fifteen characteristic layers were created to represent the spectral, thermal and temporal properties of the data set. These layers were inputs for the rule-based classification model. The model was run on three years of data, creating three single year land-cover maps. The modeling effort showed that NDVI, thermal and DEM characteristics are useful for discerning land-cover classes. The single year land-cover maps showed that the rule-based model could not detect land-cover change between years. The single year maps were combined to create a summary land-cover map. This map differs from the Brown and Lowe map in the shape, proportional size and spatial distribution of land-cover polygons. The rule-based model can discern more land-cover classes than spectral cluster classification. Ground observations and an aerial video was used to assess map accuracy. The same proportion of agreement was observed between the ground observations, the Brown and Lowe map, and the summary land-cover map. Agreement was higher between video and the summary map than between video and the Brown and Lowe map. With further refinements to the input data set, classification model rules and field accuracy assessment, higher levels of agreement can be expected. Overall results show that rule-based classification of hyper-temporal, multi-spectral satellite imagery is a desirable method for mapping global land-cover.
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Falco, Nicola. "Advanced Spectral and Spatial Techniques for Hyperspectral Image Analysis and Classification." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/369072.

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Recent advances in sensor technology have led to an increased availability of hyperspectral remote sensing images with high spectral and spatial resolutions. These images are composed by hundreds of contiguous spectral channels, covering a wide spectral range of frequencies, in which each pixel contains a highly detailed representation of the reflectance of the materials present on the ground, and a better characterization in terms of geometrical detail. The burst of informative content conveyed in the hyperspectral images permits an improved characterization of different land coverages. In spite of that, it increases significantly the complexity of the analysis, introducing a series of challenges that need to be addressed, such as the computational complexity and resources required. This dissertation aims at defining novel strategies for the analysis and classification of hyperspectral remote sensing images, placing the focal point on the investigation and optimisation techniques for the extraction and integration of spectral and spatial information. In the first part of the thesis, a thorough study on the analysis of the spectral information contained in the hyperspectral images is presented. Though, independent component analysis (ICA) has been widely used to address several tasks in the remote sensing field, such as feature reduction, spectral unmixing and classification, its employment in extracting class-discriminant information remains a research topic open to further investigation. To this extend, a profound study on the performances of different ICA algorithms is performed, highlighting their strengths and weaknesses in the hyperspectral image classification task. Based on this study, a novel approach for feature reduction is proposed, where the use of ICA is optimised for the extraction of class-specific information. In the second part of the thesis, the spatial information is exploited by employing operators from the mathematical morphology framework. Morphological operators, such as attribute profiles and their multi-channel and multi-attribute extensions, are proved to be effective in the modelling of the spatial information, dealing, however, with issues such as the high feature dimensionality, the high intrinsic information redundancy and the a-priori need for parameter tuning in filtering, which are still open. Addressing the first two issues, the reduced attribute profiles are introduced, in this thesis, as an optimised version of the morphological attribute profiles, with the property to compress all the meaningful geometrical information into a few features. Regarding the filter parameter tuning issue, an innovative strategy for automatic threshold selection is proposed. Inspired by the concept of granulometry, the proposed approach defines a novel granulometric characteristic function, which provides information on the image decomposition according to a given measure. The approach exploits the tree representation of an image, allowing us to avoid additional filtering steps prior to the threshold selection, making the process computationally effective. The outcome of this dissertation advances the state-of-the-art by proposing novel methodologies for accurate hyperspectral image classification, where the results obtained by extensive experimentation on various real hyperspectral data sets confirmed their effectiveness. Concluding the thesis, insightful and concrete remarks to the aforementioned issues are discussed.
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Falco, Nicola. "Advanced Spectral and Spatial Techniques for Hyperspectral Image Analysis and Classification." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1421/1/PhD_Nicola_Trento.pdf.

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Recent advances in sensor technology have led to an increased availability of hyperspectral remote sensing images with high spectral and spatial resolutions. These images are composed by hundreds of contiguous spectral channels, covering a wide spectral range of frequencies, in which each pixel contains a highly detailed representation of the reflectance of the materials present on the ground, and a better characterization in terms of geometrical detail. The burst of informative content conveyed in the hyperspectral images permits an improved characterization of different land coverages. In spite of that, it increases significantly the complexity of the analysis, introducing a series of challenges that need to be addressed, such as the computational complexity and resources required. This dissertation aims at defining novel strategies for the analysis and classification of hyperspectral remote sensing images, placing the focal point on the investigation and optimisation techniques for the extraction and integration of spectral and spatial information. In the first part of the thesis, a thorough study on the analysis of the spectral information contained in the hyperspectral images is presented. Though, independent component analysis (ICA) has been widely used to address several tasks in the remote sensing field, such as feature reduction, spectral unmixing and classification, its employment in extracting class-discriminant information remains a research topic open to further investigation. To this extend, a profound study on the performances of different ICA algorithms is performed, highlighting their strengths and weaknesses in the hyperspectral image classification task. Based on this study, a novel approach for feature reduction is proposed, where the use of ICA is optimised for the extraction of class-specific information. In the second part of the thesis, the spatial information is exploited by employing operators from the mathematical morphology framework. Morphological operators, such as attribute profiles and their multi-channel and multi-attribute extensions, are proved to be effective in the modelling of the spatial information, dealing, however, with issues such as the high feature dimensionality, the high intrinsic information redundancy and the a-priori need for parameter tuning in filtering, which are still open. Addressing the first two issues, the reduced attribute profiles are introduced, in this thesis, as an optimised version of the morphological attribute profiles, with the property to compress all the meaningful geometrical information into a few features. Regarding the filter parameter tuning issue, an innovative strategy for automatic threshold selection is proposed. Inspired by the concept of granulometry, the proposed approach defines a novel granulometric characteristic function, which provides information on the image decomposition according to a given measure. The approach exploits the tree representation of an image, allowing us to avoid additional filtering steps prior to the threshold selection, making the process computationally effective. The outcome of this dissertation advances the state-of-the-art by proposing novel methodologies for accurate hyperspectral image classification, where the results obtained by extensive experimentation on various real hyperspectral data sets confirmed their effectiveness. Concluding the thesis, insightful and concrete remarks to the aforementioned issues are discussed.
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Jia, Xiuping Electrical Engineering Australian Defence Force Academy UNSW. "Classification techniques for hyperspectral remote sensing image data." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Electrical Engineering, 1996. http://handle.unsw.edu.au/1959.4/38713.

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Hyperspectral remote sensing image data, such as that recorded by AVIRIS with 224 spectral bands, provides rich information on ground cover types. However, it presents new problems in machine assisted interpretation, mainly in long processing times and the difficulties of class training due to the low ratio of number of training samples to the number of bands. This thesis investigates feasible and efficient feature reduction and image classification techniques which are appropriate for hyperspectral image data. The study is reported in three parts. The first concerns a deterministic approach for hyperspectral data interpretation. Multigroup and multiple threshold spectral coding procedures, and associated techniques for spectral matching and classification, are proposed and tested. By coding on subgroups of bands using one or three thresholds, spectral searching and matching becomes simple, fast and free of the need for radiometric correction. Modifications of existing statistical techniques are proposed in the second part of the investigation A block-based maximum likelihood classification technique is developed. Several subgroups are formed from the complete set of spectral bands in the data, based on the properties of global correlation among the bands. Subgroups which are poorly correlated with each other are treated independently using conventional maximum likelihood classification. Experimental results demonstrate that, when using appropriate subgroup sizes, the new method provides a compromise among classification accuracy, processing time and available training pixels. Furthermore, a segmented, and possibly multi-layer, principal components transformation is proposed as a possible feature reduction technique prior to classification, and for effective colour display. The transformation is performed efficiently on each of the highly correlated subgroups of bands independently. Selected features from each transformed subgroup can be then transformed again to achieve a satisfactory data reduction ratio and to generate the three most significant components for colour display. Classification accuracy is improved and high quality colour image display is achieved in experiments using two AVIRIS data sets.
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Prasert, Sunyaruk. "Multi angle imaging with spectral remote sensing for scene classification." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FPrasert.pdf.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, March 2005.
Thesis Advisor(s): Richard C. Olsen. Includes bibliographical references (p. 95-97). Also available online.
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Alam, Fahim Irfan. "Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386535.

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The recent advances in aerial- and satellite-based hyperspectral imaging sensor technologies have led to an increased availability of Earth's images with high spatial and spectral resolution, which opened the door to a large range of important applications. Hyperspectral imaging records detailed spectrum of the received light in each spatial position in the image, in which each pixel contains a highly detailed representation of the reflectance of the materials present on the ground, and a better characterization in terms of geometrical details. Since different substances exhibit different spectral signatures, the abundance of informative content conveyed in the hyperspectral images permits an improved characterization of different land coverage. Therefore, hyperspectral imaging emerged as a well-suited technology for accurate image classi fication in remote sensing. In spite of that, a signi ficantly increased complexity of the analysis introduces a series of challenges that need to be addressed on a serious note. In order to fully exploit the potential offered by these sensors, there is a need to develop accurate and effective models for spectral-spatial analysis of the recorded data. This thesis aims at presenting novel strategies for the analysis and classifi cation of hyperspectral remote sensing images, placing the focal point on the investigation on deep networks for the extraction and integration of spectral and spatial information. Deep learning has demonstrated cutting-edge performances in computer vision, particularly in object recognition and classi cation. It has also been successfully adopted in hyperspectral remote sensing domain as well. However, it is a very challenging task to fully utilize the massive potential of deep models in hyperspectral remote sensing applications since the number of training samples is limited which limits the representation capability of a deep model. Furthermore, the existing architectures of deep models need to be further investigated and modifi ed accordingly to better complement the joint use of spectral and spatial contents of hyperspectral images. In this thesis, we propose three different deep learning-based models to effectively represent spectral-spatial characteristics of hyperspectral data in the interest of classifi cation of remote sensing images. Our first proposed model focuses on integrating CRF and CNN into an end-to-end learning framework for classifying images. Our main contribution in this model is the introduction of a deep CRF in which the CRF parameters are computed using CNN and further optimized by adopting piecewise training. Furthermore, we address the problem of over fitting by employing data augmentation techniques and increased the size of the training samples for training deep networks. Our proposed 3DCNN-CRF model can be trained to fully exploit the usefulness of CRF in the context of classi fication by integrating it completely inside of a deep model. Considering that the separation of constituent materials and their abundances provide detailed analysis of the data, our second algorithm investigates the potential of using unmixing results in deep models to classify images. We extend an existing region based structure preserving non-negative matrix factorization method to estimate groups of spectral bands with the goal to capture subtle spectral-spatial distribution from the image. We subsequently use these important unmixing results as input to generate superpixels, which are further represented by kernel density estimated probability distribution function. Finally, these abundance information-guided superpixels are directly supplied into a deep model in which the inference is implicitly formulated as a recurrent neural network to perform the eventual classifi cation. Finally, we perform a detailed investigation on the possibilities of adopting generative adversarial models into hyperspectral image classifi cation. We present a GAN-based spectral-spatial method that primarily focuses on signifi cantly improving the multiclass classi cation ability of the discriminator of GAN models. In this context, we propose to adopt the triplet constraint property and extend it to build a useful feature embedding for remote sensing images for use in classi cation. Furthermore, our proposed Triplet- 3D-GAN model also includes feedback from discriminator's intermediate features to improve the quality of the generator's sample generation process.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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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
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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Behmo, Régis. "Visual feature graphs and image recognition." Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00545419.

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La problèmatique dont nous nous occupons dans cette thèse est la classification automatique d'images bidimensionnelles, ainsi que la détection d'objets génériques dans des images. Les avancées de ce champ de recherche contribuent à l'élaboration de systèmes intelligents, tels que des robots autonomes et la création d'un web sémantique. Dans ce contexte, la conception de représentations d'images et de classificateurs appropriés constituent des problèmes ambitieux. Notre travail de recherche fournit des solutions à ces deux problèmes, que sont la représentation et la classification d'images. Afin de générer notre représentation d'image, nous extrayons des attributs visuels de l'image et construisons une structure de graphe basée sur les propriétés liées au relations de proximités entre les points d'intérêt associés. Nous montrons que certaines propriétés spectrales de ces graphes constituent de bons invariants aux classes de transformations géométriques rigides. Notre représentation d'image est basée sur ces propriétés. Les résultats expérimentaux démontrent que cette représentation constitue une amélioration par rapport à d'autres représentations similaires, mais qui n'intègrent pas les informations liées à l'organisation spatiale des points d'intérêt. Cependant, un inconvénient de cette méthode est qu'elle fait appel à une quantification (avec pertes) de l'espace des attributs visuels afin d'être combinée avec un classificateur Support Vecteur Machine (SVM) efficace. Nous résolvons ce problème en créant un nouveau classificateur, basé sur la distance au plus proche voisin, et qui permet la classification d'objets assimilés à des ensembles de points. La linéarité de ce classificateur nous permet également de faire de la détection d'objet, en plus de la classification d'images. Une autre propriété intéressante de ce classificateur est sa capacité à combiner différents types d'attributs visuels de manière optimale. Nous utilisons cette propriété pour formuler le problème de classification de graphes de manière différente. Les expériences, menées sur une grande variété de jeux de données, montrent les bénéfices quantitatifs de notre approche.
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Tso, Brandt C. K. "An investigation of alternative strategies for incorporating spectral, textural, and contextual information in remote sensing image classification." Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387663.

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Rajadell, Rojas Olga. "Data selection and spectral-spatial characterisation for hyperspectral image segmentation. Applications to remote sensing." Doctoral thesis, Universitat Jaume I, 2013. http://hdl.handle.net/10803/669093.

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El análisis de imágenes ha impulsado muchos descubrimientos en la ciencia actual. Esta tesis se centra en el análisis de imágenes remotas para inspección aérea, exactamente en el problema de segmentación y clasificación de acuerdo al uso del suelo. Desde el nacimiento de los sensores hiperespectrales su uso ha sido vital para esta tarea ya que facilitan y mejoran sustancialmente el resultado. Sin embargo el uso de imágenes hiperespectrales entraña, entre otros, problemas de dimensionalidad y de interacción con los expertos. Proponemos mejoras que ayuden a paliar estos inconvenientes y hagan el problema mas eficiente.
Lately image analysis have aided many discoveries in research. This thesis focusses on the analysis of remote sensed images for aerial inspection. It tackles the problem of segmentation and classification according to land usage. In this field, the use of hyperspectral images has been the trend followed since the emergence of hyperspectral sensors. This type of images improves the performance of the task but raises some issues. Two of those issues are the dimensionality and the interaction with experts. We propose enhancements overcome them. Efficiency and economic reasons encouraged to start this work. The enhancements introduced in this work allow to tackle segmentation and classification of this type of images using less data, thus increasing the efficiency and enabling the design task specific sensors which are cheaper. Also, our enhacements allow to perform the same task with less expert collaboration which also decreases the costs and accelerates the process.
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Books on the topic "HYPER SPECTRAL IMAGE CLASSIFICATION"

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Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers, 2003.

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Chang, Chein-I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer, 2003.

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Book chapters on the topic "HYPER SPECTRAL IMAGE CLASSIFICATION"

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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, 587–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_51.

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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, 3–10. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50212-0_1.

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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, 863–69. Cham: 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, 861–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29516-5_65.

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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, 439–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12990-2_50.

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Gadhave, Rajashree, and R. R. Sedamkar. "Automated Classification of Hyper Spectral Image Using Supervised Machine Learning Approach." In Lecture Notes in Electrical Engineering, 763–75. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4831-2_63.

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Panchal, Soumyashree M., and Shivaputra. "Object Classification from a Hyper Spectral Image Using Spectrum Bands with Wavelength and Feature Set." In Software Engineering and Algorithms, 340–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77442-4_29.

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Vatsavayi, Valli Kumari, Saritha Hepsibha Pilli, and Charishma Bobbili. "Performance Analysis of Discrete Wavelets in Hyper Spectral Image Classification: A Deep Learning Approach." In Proceedings of International Conference on Computational Intelligence and Data Engineering, 387–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0609-3_27.

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Raju, Kalidindi Kishore, G. P. Saradhi Varma, and Davuluri Rajyalakshmi. "A Comprehensive Review on Effect of Band Selection on the Recital of Hyper-spectral Image Classification." In Lecture Notes in Electrical Engineering, 303–20. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3828-5_33.

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Mei, Zhiming, Long Wang, and Cen Guo. "Hyper-spectral Images Classification Based on 3D Convolution Neural Networks for Remote Sensing." In Communications in Computer and Information Science, 205–14. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5937-8_21.

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Conference papers on the topic "HYPER SPECTRAL IMAGE CLASSIFICATION"

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Shabna, A., and R. Ganesan. "HSEG and PCA for hyper-spectral image classification." In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, 2014. http://dx.doi.org/10.1109/iccicct.2014.6992927.

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Sharma, Sanatan, Akashdeep Goel, Omkar Gune, Biplab Banerjee, and Subhasis Chaudhuri. "Class Specific Coders for Hyper-Spectral Image Classification." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451637.

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Mahendren, Sutharsan, Tharindu Fernando, Sridha Sridharan, Peyman Moghadam, and Clinton Fookes. "Reduction of Feature Contamination for Hyper Spectral Image Classification." In 2021 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2021. http://dx.doi.org/10.1109/dicta52665.2021.9647153.

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Prigent, Sylvain, Xavier Descombes, Didier Zugaj, and Josiane Zerubia. "Spectral analysis and unsupervised SVM classification for skin hyper-pigmentation classification." In 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2010. http://dx.doi.org/10.1109/whispers.2010.5594917.

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Thapliyal, Ankita. "Deep Intensified Archetypical CNN Approach for Hyper Spectral Image Classification." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544875.

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Thapliyal, Ankita. "Deep Intensified Archetypical CNN Approach for Hyper Spectral Image Classification." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544875.

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Sawant, Shrutika S., and M. Prabukumar. "Semi-supervised techniques based hyper-spectral image classification: A survey." In 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2017. http://dx.doi.org/10.1109/ipact.2017.8244999.

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Su, Zhenyu, and xiuying zhao. "Using deep learning in image hyper spectral segmentation, classification, and detection." In Fourth Seminar on Novel Optoelectronic Detection Technology and Application, edited by Weiqi Jin and Ye Li. SPIE, 2018. http://dx.doi.org/10.1117/12.2307376.

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Mallapragada, Srivatsa, and Chih-Cheng Hung. "Statistical Perspective of SOM and CSOM for Hyper-Spectral Image Classification." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9324200.

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Ullah, Shan, and Deok-Hwan Kim. "Benchmarking Jetson Platform for 3D Point-Cloud and Hyper-Spectral Image Classification." In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2020. http://dx.doi.org/10.1109/bigcomp48618.2020.00-21.

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Reports on the topic "HYPER SPECTRAL IMAGE CLASSIFICATION"

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Guindon, B. Combining Diverse Spectral, Spatial and Contextual Attributes in Segment-Based Image Classification. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219634.

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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, October 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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Delwiche, Michael, Yael Edan, and Yoav Sarig. An Inspection System for Sorting Fruit with Machine Vision. United States Department of Agriculture, March 1996. http://dx.doi.org/10.32747/1996.7612831.bard.

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Concepts for real-time grading of fruits and vegetables were developed, including multi-spectral imaging with structured illumination to detect and distinguish surface defects from concavities. Based on these concepts, a single-lane conveyor and inspection system were designed and evaluated. Image processing algorithms were developed to inspect and grade large quasi-spherical fruits (peaches and apples) and smaller dried fruits (dates). Adjusting defect pixel thresholds to achieve a 25% error rate on good apples, classification errors for bruise, crack, and cut classes were 51%, 42%, and 46%, respectively. Comparable results for bruise, scar, and cut peach clases were 48%, 22%, and 58%, respectively. Acquiring more than two images of each fruit and using more than six lines of structured illumination per fruit would reduce sorting errors. Doing so, potential sorting error rates for bruise, crack, and cut apple classes were estimated to be 38%, 38%, and 33%, respectively. Similarly, potential error rates for the bruitse, scar, and cut peach classes were 9%, 3%, and 30%, respectively. Date size classification results were good: 68% within one size class and 98% within two size classes. Date quality classification results were not adequate due to the problem of blistering. Improved features were discussed. The most significant contribution of this research was the on-going collaboration with producers and equipment manufacturers, and the resulting transfer of research ideas to expedite the commercial application of machine vision for postharvest inspection and grading of agricultural products.
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Bonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7696531.bard.

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To reduce financial risk and N losses to the environment, fertilization methods are needed that improve NUE and increase the quality of wheat. In the literature, ample attention is given to grid-based and zone-based soil testing to determine the soil N available early in the growing season. Plus, information is available on in-season N topdressing applications as a means of improving GPC. However, the vast majority of research has focused on wheat that is grown under N limiting conditions in sub-humid regions and irrigated fields. Less attention has been given to wheat in dryland that is water limited. The objectives of this study were to: (1) determine accuracy in determining GPC of HRSW in Israel and SWWW in Oregon using on-combine optical sensors under field conditions; (2) develop a quantitative relationship between image spectral reflectance and effective crop physiological parameters; (3) develop an operational precision N management procedure that combines variable-rate N recommendations at planting as derived from maps of grain yield, GPC, and test weight; and at mid-season as derived from quantitative relationships, remote sensing, and the DSS; and (4) address the economic and technology-transfer aspects of producers’ needs. Results from the research suggest that optical sensing and the DSS can be used for estimating the N status of dryland wheat and deciding whether additional N is needed to improve GPC. Significant findings include: 1. In-line NIR reflectance spectroscopy can be used to rapidly and accurately (SEP <5.0 mg g⁻¹) measure GPC of a grain stream conveyed by an auger. 2. On-combine NIR spectroscopy can be used to accurately estimate (R² < 0.88) grain test weight across fields. 3. Precision N management based on N removal increases GPC, grain yield, and profitability in rainfed wheat. 4. Hyperspectral SI and partial least squares (PLS) models have excellent potential for estimation of biomass, and water and N contents of wheat. 5. A novel heading index can be used to monitor spike emergence of wheat with classification accuracy between 53 and 83%. 6. Index MCARI/MTVI2 promises to improve remote sensing of wheat N status where water- not soil N fertility, is the main driver of plant growth. Important features include: (a) computable from commercial aerospace imagery that include the red edge waveband, (b) sensitive to Chl and resistant to variation in crop biomass, and (c) accommodates variation in soil reflectance. Findings #1 and #2 above enable growers to further implement an efficient, low cost PNM approach using commercially available on-combine optical sensors. Finding #3 suggests that profit opportunities may exist from PNM based on information from on-combine sensing and aerospace remote sensing. Finding #4, with its emphasis on data retrieval and accuracy, enhances the potential usefulness of a DSS as a tool for field crop management. Finding #5 enables land managers to use a DSS to ascertain at mid-season whether a wheat crop should be harvested for grain or forage. Finding #6a expands potential commercial opportunities of MS imagery and thus has special importance to a majority of aerospace imaging firms specializing in the acquisition and utilization of these data. Finding #6b on index MCARI/MVTI2 has great potential to expand use of ground-based sensing and in-season N management to millions of hectares of land in semiarid environments where water- not N, is the main determinant of grain yield. Finding #6c demonstrates that MCARI/MTVI2 may alleviate the requirement of multiple N-rich reference strips to account for soil differences within farm fields. This simplicity will be less demanding of grower resources, promising substantially greater acceptance of sensing technologies for in-season N management.
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