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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery V"

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Land Use Land Cover Classification Using Multi-spectral Satellite imagery 5th

Land Use Land Cover Classification Using Multi-spectral Sate...

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5th International conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Mohammad, Neelofar Jaha vaddi, Radhesyam Neelam, B. v. D. Soujitha Cheepulla, Hemanth Department of IT VR Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9783031609343

Land cover change detection using multispectral images is a critical application in remote sensing. It involves monitoring and assessing alterations in the Earth’s surface’s physical and biological characteristics over time. multispectral images acquired by satellite sensors, which capture data across various electromagnetic spectrum bands, enable the identification and tracking of diverse land cover types, including forests, crops, urban areas, water bodies, and barren land. The fundamental concept behind change detection is the comparison of two temporally separated satellite images to identify discrepancies between the two timestamps. To achieve this, two distinct approaches have been implemented. The first approach utilizes K-Means clustering, which categorizes pixels in the images into clusters based on their spectral attributes, generating color maps that highlight areas of change. In the second approach, Gaussian Mixture Models (GMM) are employed to model pixel value distributions, resulting in similar color maps that identify regions of land cover change. These algorithms play a crucial role in producing visual representations of where alterations in land cover have occurred, facilitating applications in environmental management, urban planning, and agriculture. In summary, land cover change detection through multispectral images and remote sensing techniques is essential for monitoring Earth’s surface changes over time. Two approaches, K-Means clustering and Gaussian Mixture Models, have been implemented to identify changes and generate colour maps that highlight areas of interest in land cover transformations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Satellite imagery

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Iterative R&R (Rotation and Remarginalization) for Detecting Targets in Spectral imagery 25

Iterative R&R (Rotation and Remarginalization) for Detecting...

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conference on Imaging Spectrometry XXv - Applications, Sensors, and Processing

作者： Theiler, James Ren, Christopher X. Los Alamos Natl Lab Los Alamos NM 87545 USA Earthrise Media Oakland CA USA

ISBN: (纸本)9781510654556;9781510654549

We explore some variants of \Gaussianization" for characterizing the distribution of background pixels in multispectral and hyperspectral imagery, and then use this characterization to develop algorithms for target detection. We consider two very different problems {anomalous change detection and gas-phase plume detection {as ways to explore the applicability of Gaussianization for remote sensing image analysis. One variant is an extension of the Gaussianization concept to non-Gaussian reference distributions, and in particular, we show that using the multivariate t as the reference distribution often leads to better target detection performance. Since we are no longer, strictly speaking, Gauss-ianizing, we call the method iterative rotation and remarginalization. In our scheme, the remarginalization is achieved with a parametric transformation function that is built up from a linear basis of (hard or soft) hinge functions, which provide explicitly differentiable and enforcably monotonic remarginalization functions. An efficient knot-pruning strategy enables rapid training of these functions. Also, for remote sensing imagery with many spectral channels, we have found it advantageous to pre-whiten the data with axes aligned to principal components, and then selectively to Gaussianize only the top principal components, treating the lower-variance directions as \already Gaussian." This provides a computationally faster and empirically more effective Gaussianization for spectral imagery.

关键词： remote sensing target detection multispectral imagery machine learning density estimation Gaussianization remarginalization

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multispectral imaging system for short wave infrared applications 28

Multispectral imaging system for short wave infrared applica...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvIII

作者： Rosenberger, M. Dittrich, P-G Illmann, R. Horn, R. Golomoz, A. Notni, G. Eiser, S. Hirst, O. Jahn, N. Tech Univ Ilmenau Grp Qual Assurance & Ind Image Proc Gustav Kirchhoff Pl 2 Ilmenau Germany Mrb Automat GmbH Werner v Siemens Str 7 Ilmenau Germany SQB GmbH Werner v Siemens Str 9 Ilmenau Germany

ISBN: (纸本)9781510650657;9781510650640

A lot of applications as well as in the laboratory range and industrial range need a short-wave infrared imaging system. Especially multispectral imaging in that wavelength region are often enable new applications for quality assurance and monitoring of industrial processes. Due to the cost of the SWIR image sensors a multispectral imaging system should be flexible and adjustable to realize a maximum of applications. Normally push broom devices will fulfill these requirements. Disadvantages are that the sample must be moved, the spectral crosstalk and the blurring between the different channels can disturb the processing, and the correctness of spatial resolution along the scanning direction lead to uncertainties for dimensional measurements. For that reasons a twelve-channel filter-based imaging system was designed. A motor driven filter-wheel with a high precision drive will ensure that the filters will be placed in front of a SWIR Image sensor very precisely. To compensate the spectral aberrations along the optical axis a second drive positioning the image sensor into the focus plane. This enable sharp images in all spectral channels as well as high SNR (depends also on the capability of the SWIR Sensor). Furthermore, this implementation allows also to focus without changing the adjustment of the objective lens. A special designed dome light using halogen bulbs delivers very homogeneous radiation on the sample table. For the image readout, the control of the drives as well as the image presentation a software with a graphical user interface was developed. To export the image stacks in 3rd party software the imaging software saves the multispectral images in the envi-format.

关键词： SWIR multispectral imaging imaging system

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Supervised Unconstrained and Constrained Least Squares Unmixing in hyperspectral imagery 27

Supervised Unconstrained and Constrained Least Squares Unmix...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvII

作者： Heinz, Daniel C. Bahr, Thomas Terrie, Greg Harris Geospatial Solut Inc Herndon VA 20170 USA Harris Geospatial Solut GmbH D-82205 Gilching Germany

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

Spectral mixture analysis of hyperspectral imagery allows for detection, classification and quantification of targets present in the imaged scene. It can be difficult to characterize the performance of spectral unmixing techniques at detection, classification and quantification of field data at the subpixel level due to limited ground truth, especially for mixed pixels. Fortunately, the SpecTIR hyperspectral Airborne Experiment (SHARE) 2012 contains a set of targets specifically designed to test spectral unmixing algorithms. In this paper we explore the performance of an unconstrained and three constrained least squares methods for supervised spectral unmixing. Each of the three methods provides an estimate of the abundance of known targets which can be used for detection, classification and quantification. A detailed evaluation of these spectral unmixing techniques on the SHARE 2012 hyperspectral data is used to demonstrate the performance of each method at supervised target detection, classification and quantification.

关键词： UnConstrained Abundance (UCA) Sum-to-One Constrained Abundance (SCA) Nonnegatively Constrained Abundance (NCA) Fully Constrained Abundance (FCA) linear spectral mixture analysis (LSMA) SpecTIR hyperspectral Airborne Experiment (SHARE) 2012

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Subpixel Level Discrimination of vegetable Crops in a Complex Landscape Environment

Subpixel Level Discrimination of Vegetable Crops in a Comple...

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International conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)

作者： Kumar, Manohar C. v. S. S. Nidamanuri, Rama Rao Dadhwal, vinay Kumar Indian Inst Space Sci & Technol Dept Earth & Space Sci Thiruvananthapuram Kerala India Natl Inst Adv Studies Environm Sci Nat Sci & Engn Bengaluru Karnataka India

ISBN: (纸本)9798350345421

Undeterred anthropogenic activities in the forest ecosystem of India have been altering the spatial distribution and species composition of the forest ecosystem. Mitigation of human activities in deep and fragile ecosystems in global biodiversity hotspots such as the Western Ghats requires robust detection, mapping, and monitoring techniques. The cultivation of vegetable crops is an undeniable proxy for the existence of human activities in a dense forest landscape. As these crops are cultivated for personal consumption, the areal extent and spatial distribution are limited to the backyard, shifting, or terrain cultivation with areal coverage of only several square meters. Remote sensing has been extensively used for the mapping and composition classification of forests. Supervised classification is the method of choice for mapping various land cover categories including forest species. However, when there are very few or no reference pixels available in the imagery for training, the classification approach fails to label pixels. Sub-pixel level spectral mixture modeling has the potential to map sparsely distributed land covers even if there are very few or no reference pixels in the imagery. When it is not feasible to identify unambiguous reference pixels in the imagery, spectral mixture modeling, in principle, permits the infusion of independent reference spectra from a spectral library for sub-pixel lever mapping. We have explored the potential of detecting vegetable crops whose spatial distribution is very sparse and are in a complex forest landscape. For this, we adopted and implemented several sparsity-based spectral unmixing algorithms for the targeted detection and mapping of vegetable crops ( banana and bitter gourd). We have implemented the methodology on airborne hyperspectral imagery acquired over the Mudumalai national forest region of the Western Ghats, India. The quality of detection suggests the possibility of mapping vegetable crops even if they occu

关键词： hyperspectral remote sensing spectral library spectral unmixing abundance estimation

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Acacia dealbata classification from aerial imagery acquired using unmanned aerial vehicles

Acacia dealbata classification from aerial imagery acquired ...

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2022 International conference on ENTERprise Information Systems, CENTERIS 2022 - International conference on Project MANagement, ProjMAN 2022 and International conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Pinto, Jorge Sousa, Antonio Sousa, Joaquim J. Peres, Emanuel Padua, Luis Vila Real5000-801 Portugal Porto4200-465 Portugal UTAD Vila Real5000-801 Portugal

Non-native plant species can have a negative impact in the ecosystems and in local economies when they spread uncontrollably. Monitoring tools can support their management and spread. In this paper, an exploratory approach is presented for pixelwise detection of Acacia dealbata from UAv-based imagery acquired from RGB and multispectral sensors. Four machine learning algorithms-k-nearest neighbors (KNN), random forest (RF), adaptive boosting (AdaBoost) and a linear kernel SvM (LSvM)-Are trained using four datasets (hue, saturation and value-HSv, multispectral-MSP, RGB and a combination of all features) and their classification performance is evaluated. RF classifier obtained the overall best performance, with an accuracy above 86% in all data combinations, with LSvM showing the poorer results. Obtained results are encouraging for monitoring invasive species and can serve as a base for future improvements to detect invasive species. © 2023 Elsevier B.v.. All rights reserved.

关键词： Adaptive boosting

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Multi-resolution hyperspectral collection for analysis of panchromatic sharpening algorithms 27

Multi-resolution hyperspectral collection for analysis of pa...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvII

作者： Carrock, Joseph A., II Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

Assessment of panchromatic sharpening algorithms typically starts with a high resolution hyperspectral Image (HSI), which is then spatially degraded so that after the sharpening process, the result can be compared to the original and analyzed for accuracy. This leads to questions about quantitative assessments based solely on simulated low resolution data. To address this problem, a multi-resolution hyperspectral data set was collected by researchers from the Rochester Institute of Technology (RIT) in Henrietta, NY on July 24th 2020. imagery of 48 felt targets, ranging in size from 5 cm to 30 cm and in six different colors, was collected using RIT's MX-1 UAS imaging system, which is designed to collect 272 spectral bands from approximately 400nm to 1000nm. Three flights were performed and images of the target scene were collected at flight altitudes of 30m, 60m, and 120m. The resulting imagery possesses ratios of 2:1 and 4:1 spatial resolution relative to the lowest altitude flight. The goal of this imaging campaign was to create a data set that will be used to test hyperspectral pansharpening algorithms currently under development at RIT. The radiometric accuracy of sharpening algorithms can be better ascertained through quantitative analysis of their results after being applied to this non-simulated multi-resolution hyperspectral data set. This presentation will summarize the process of planning, creating, collecting and packaging the dataset which will be provided to RIT researchers and will also available for download online through RIT.

关键词： panchromatic sharpening hyperspectral imagery multisensor fusion

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Improved Sub-pixel Material Identification with Spline-Based Spectral Smoothing 28

Improved Sub-pixel Material Identification with Spline-Based...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvIII

作者： McElhinney, O. Pieper, M. L. Manolakis, D. Ingle, v Loughlin, C. Bostick, Randall Weisner, A. MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA Northeastern Univ 360 Huntington Ave Boston MA 02115 USA Natl Air & Space Intelligence Ctr Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510650657;9781510650640

hyperspectral imagers allow for the identification of materials of interest (MOI) in a scene using spectroscopic analysis. Material identification is made more difficult when the MOI fills a fraction of the pixel. The resulting pixel spectrum is a linear mixture of the MOI and its surrounding background with weights/abundances based on the fraction of each material within the pixel. Some identification methods utilize pixel unmixing to match a library spectrum to the suspected MOI. The resulting quality of fit depends on how closely the library spectrum matches that of the MOI. Further analysis is accomplished by removing the background portion, and comparing the normalized MOI portion to the library spectrum. When the library spectrum corresponds to the MOI, the two spectra should visually match, with the exception of noise. Often, spectral smoothing is required to improve the match as the SNR of the MOI portion decreases with its abundance. We propose a spline-based smoothing method to reduce noise error greatly while maintaining the fine spectral features used to distinguish between spectrally similar materials. The main practical difficulty of spline smoothing lies in setting the parameter which determines the amount of smoothing. We show that automatically setting the smoothing parameter, such that the roughness of the smooth MOI portion matches the library spectrum, prevents over smoothing and improves identification.

关键词： hyperspectral imaging spline functions smoothing

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Subpixel target implantation to assess pansharpening performance on hyperspectral datasets 27

Subpixel target implantation to assess pansharpening perform...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvII

作者： Ducay, Rey Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

Target detection is one of the most important applications utilizing the rich spectral information from hyperspectral imaging systems. Data fusion algorithms applied on hyperspectral datasets address the inherent spatial-spectral resolution tradeoff in these imaging systems by combining spectral information from hyperspectral data with spatial information from hi-res panchromatic or multispectral images (e.g., hi-res RGB). This paper presents the first attempt at using an iterative target implantation technique as a modification to Wald's protocol to assess the performance of data fusion algorithms in target detection tasks. More specifically, this paper looks at how the sharpening process localizes and discriminates the subpixel target from its background, and characterizes an image-wide detectability of any single subpixel target independent of location in the image. We used NNDiffuse as our pansharpener to perform HRPAN+LRHSI data fusion and the adaptive coherence estimator (ACE) as our target detector. Results show that our methodology is effective at assessing (1) how the sharpening process enhances target-background separability within any 5 x 5 window anywhere on the image and (2) how the sharpening process enhances the detectability of a single subpixel target over the entire hyperspectral image.

关键词： hyperspectral imagery ACE target detection panchromatic sharpening target implantation Wald's protocol NNDiffuse

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Deepfaking it: experiments in generative, adversarial multispectral remote sensing 27

Deepfaking it: experiments in generative, adversarial multis...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvII

作者： Ren, Christopher X. Ziemann, Amanda Theiler, James Moore, Juston Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA Los Alamos Natl Lab Adv Res Cyber Syst Los Alamos NM USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

In this work we utilize generative adversarial networks (GANs) to synthesize realistic transformations for remote sensing imagery in the multispectral domain. Despite the apparent perceptual realism of the transformed images at a first glance, we show that a deep learning classifier can very easily be trained to differentiate between real and GAN-generated images, likely due to subtle but pervasive artifacts introduced by the GAN during the synthesis process. We also show that a very low-amplitude adversarial attack can easily fool the aforementioned deep learning classifier, although these types of attacks can be partially mitigated via adversarial training. Finally, we explore the features utilized by the classifier to differentiate real images from GAN-generated ones, and how adversarial training causes the classifier to focus on different, lower-frequency features.

关键词： Deep learning Generative Adversarial Networks Machine learning Remote Sensing multispectral imagery

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