Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 28 for “"Hyperspectral images"”.
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Deep Learning-Based Synthesis of Surgical Hyperspectral Images
… from underlying physiological tissue properties, hyperspectral imaging (HSI) together with machine learning-based analyses has been proposed as a solution in recent literature. However, HSI data in the clinical setting is sparse, as its acquisition is crucially limited by a small number of …
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Advanced Techniques for Automatic Change Detection in Multitemporal Hyperspectral Images
… of the new generation remote sensing satellite hyperspectral images provides an important data source for Earth Observation (EO). Hyperspectral images are characterized by a very detailed spectral sampling (i.e., very high spectral resolution) over a wide spectral wavelength range. This …
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Advanced Methods for Change Detection in LiDAR Data and Hyperspectral Images
… Light Detection And Ranging (LiDAR) and Hyperspectral (HS) sensors acquire data that accurately characterize the 3-D structure and the spectral signature of the area of interest, respectively. With the upcoming generation of small sensors designed for Unmanned Aerial Vehicle (UAVs) and …
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Advanced modelling and analytics for effective change and anomaly detection in hyperspectral images.
… novel models and analytics techniques for hyperspectral change detection and anomaly detection. With the widespread applications of hyperspectral imagery (HSI) in fields such as remote sensing, environmental monitoring and agriculture, the need for accurate and efficient change detection …
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Explainable artificial intelligence and deep reconstruction of hyperspectral images for advancing sweetpotato quality evaluation
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
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A neural relevance model for feature extraction from hyperspectral images, and its application in the wavelet domain
… 23 classes of a real 194-band remotely sensed hyperspectral image. The higher the data dimension and/or larger the number of classes, the more advantage GRLVQI shows over GRLVQ. The improved performance of GRLVQI over GRLVQ is substantiated using several different methods discussed in the …
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Hyperspectral image compression using implicit neural representations
Hyperspectral images (HSI) capture the full electromagnetic spectrum for each pixel in a scene. They often hold hundreds of channels per pixel, providing significantly more information compared to a comparably sized RGB color image. As the cost of obtaining these images decreases, there is a need …
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Superpixels for Hyperspectral Image Analysis
… and robustly exploits superpixels for hyperspectral data. We study and quantify the efficacy of state-of-the-art superpixel generation algorithms for a variety of hyperspectral images. In this work, superpixel level analysis is proposed for two different hyperspectral image analysis …
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Anomaly detection in hyperspectral signatures using automated derivative spectroscopy methods
… was to detect anomalies in remotely sensed Hyperspectral images using automated derivative based methods. A database of Hyperspectral signatures was used that had simulated additive Gaussian anomalies that modeled a weakly concentrated aerosol in several spectral bands. The automated pattern …
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Land Cover Quantification using Autoencoder based Unsupervised Deep Learning
… model for land cover quantification through hyperspectral unmixing using an unsupervised autoencoder. Land cover identification and classification is instrumental in urban planning, environmental monitoring and land management. With the technological advancements in remote sensing, …
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Fast algorithms for wavelet-based analysis of hyperspectral signatures
Hyperspectral sensors promise great improvements in the quality of information gathered for remote sensing applications. However, they also present a huge challenge to data storage and computing systems. Thus there is a great need for reliable compression schemes, as well as analysis tools that can …
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Detecting anomalies in remotely sensed hyperspectral signatures via wavelet transforms
… designed and tested for use with remotely sensed hyperspectral images. A database of hyperspectral signatures was created to test the system using a variety of Gaussian shaped targets. The signal-to-noise ratio of the targets varied from -95dB to -50dB. The system utilizes a wavelet-based method …
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Integrated management of the invasive weed, cut-leaved teasel (Dipsacus laciniatus L.) along a Missouri highways
… pattern and persistence were conducted. Hyperspectral images were collected using airplane along I-70 to assess teasel infestation levels. Chemical control together with grass establishment was utilized to reduce severity of infestations. Cut-leaved teasel produced germinable seed 12 days …
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Advancement of fiber image guide based snapshot imaging spectrometer technology for bioimaging and environmental applications
Hyperspectral imaging spectrometers provide detailed and continuous spectral data in addition to spatial information found in commercial RGB or monochromatic cameras. The addition of spectral information can provide valuable insight in many fields such as but not limited to biomedical imaging, …
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Deep learning applications in hyperspectral imaging for agriculture: image reconstruction and model design for quality prediction
Non-invasive techniques, such as hyperspectral imaging (HSI), are crucial for analyzing the detailed chemical and structural composition of agricultural products. By capturing both spectral and spatial information simultaneously, HSI enables advanced analysis of key quality attributes in …
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Deep Learning on Hyperspectral Image Classification
Classification of Hyperspectral images is one of the main problems in the research field of Remote Sensing and other applications developed through computer vision. With the advantage of spectral and spatial information, it is possible to distinguish effectively different materials on the surface. …
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A quantitative characterization of tuberculin skin test indurations using hyperspectral imaging to enable automated latent tuberculosis screening
… understanding of LTBI. Recent applications of hyperspectral imaging (HSI) in other dermatological applications provides evidence of its viability in addressing the shortcomings of the TST. Thus, the aim of this research was to develop and validate an HSI-based approach for quantitative …
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Enhancing dimensionality in remote sensing images
… surface. These modalities include panchromatic images, which are single-band grayscale representations; multispectral images, capturing a limited number of spectral bands; and hyperspectral images, encompassing a broad spectrum with numerous spectral bands. Images of the same scene are often …
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Weak-Supervised Deep Learning Methods for the Analysis of Multi-Source Satellite Remote Sensing Images
… of multi-source satellite remote sensing images. By employing novel deep learning techniques and cutting-edge methodologies, this thesis endeavors to contribute to advancements in the field of remote sensing. In this thesis, the problems related to limited labels are solved in several ways …
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Evaluation of Ground-Based Spectral Imaging Capabilities for Detecting and Quantifying Landfill Methane Emissions
… the course of two weeks in the summer of 2024, hyperspectral images were collected from a distance of 2 km to 7 km and with a scanning frequency of approximately one minute. A matched filter was used to retrieve methane column enhancements from the 2.3-µm spectral region of these observations. …
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