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Advances in Computing, Communication and Applied Informatics (ACCAI), International conference on

作者： J Kavipriya G Vadivu Data Science and Business Systems SRM institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Planning for agriculture, allocating resources, and ensuring food security all depend on the accurate and timely estimate of the crop yield. This paper investigates the use of Machine Learning (ML) and Deep Learning methods (DL) to forecast crop yield using remote sensing images. multispectral and hyperspectral images from remote sensing provide a useful source of data for tracking crop health and estimating prospective yield. This study scrutinizes the advantage of the abundant spectral and spatial data that remote sensing sensors have collected. Convolutional neural networks (CNNs) and Machine learning are utilized for feature extraction and prediction, leveraging the hierarchical representations acquired from remote sensing data. A dataset of ground-truth yield data and historical satellite photos are used to train the DL and ML algorithms. In order to improve the model’s generalization to various geographic locations and cropping seasons, transfer learning techniques can also be used. Moreover, interpretable deep learning methods and attention mechanisms are investigated to improve the model’s transparency and reveal key elements that influence yield prediction. By giving farmers, decision-makers, and other stakeholders timely and precise information on crop output estimates, the research’s findings have the potential to greatly advance precision agriculture methods. Deep learning methods combined with satellite imagery show approximately $\mathbf{9 0 \%}$ accuracy for scalable and effective agricultural production monitoring, which will ultimately help sustainable farming methods.

关键词： Deep learning Precision agriculture Satellites Production Predictive models Generative adversarial networks Data models

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Unsupervised Learning for Land Cover Mapping of Casablanca Using multispectral Imaging

Unsupervised Learning for Land Cover Mapping of Casablanca U...

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Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), ASU International conference in

作者： Hafsa Ouchra Abdessamad Belangour Allae Erraissi Laboratory of Information Technology and Modeling LTIM Faculty of Sciences Ben M'sik Hassan II University Casablanca Morocco Polydisciplinary Faculty of Sidi Bennour Chouaib Doukkali University El Jadida Morocco

Precise and current land use data hold immense significance in facilitating efficient urban planning and appropriate environmental oversight. This paper proposes an approach to the unsupervised classification of Casablanca's land use using the Google Earth Engine (GEE) platform. The study relies on multispectral satellite imagery, in particular data from Landsat satellites, to extract meaningful land use categories without resorting to manual labeling. The operational process includes data collection, pre-processing, unsupervised clustering, and graphical display of results. By applying the k-means and Lvq clustering algorithms, the urban area is split into distinct groups, each representing a specific land use class. The resulting land use map provides valuable data on Casablanca's urban fabric, highlighting wooded areas, agricultural land, built infrastructure, water bodies, and barren land. This automated approach demonstrates GEE's potential as a powerful tool for analyzing land use, enabling informed, data-driven decisions on urban development and environmental monitoring. The methodology outlined can serve as a reference for similar research in other regions, helping to advance remote sensing and geospatial analysis techniques in urban and environmental studies. The effectiveness of these two algorithms is assessed in terms of overall accuracy and kappa coefficient. The k-means algorithm showed moderate accuracy, while the Lvq algorithm showed the least satisfactory results.

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Comparative performance of machine learning algorithms for Forest Cover classification using ASI - PRISMA hyperspectral data

Comparative performance of machine learning algorithms for F...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Eros Caputi Gabriele Delogu Alessio Patriarca Miriam Perretta Lorenzo Gatti Lorenzo Boccia Maria Nicolina Ripa Department: Deim Università Degli Studi Della Tuscia Viterbo Italy Department: Dafne Università Degli Studi Della Tuscia Viterbo Italy Department: Diarc Univesità di Napoli Federico II Napoli Italy

Remote sensing (RS) supports studies on land cover and land cover changes, agroforestry planning and helps in performing cross-checks in the framework of EU’s common agricultural policy (CAP). multispectral data (with limited number of spectral bands) and multi-temporal processing (sequence of scenes captured on different periods of time) were mainly used for RS. The use of hyperspectral imagery such as those provided by the PRISMA satellite, could enable further advances. This study aims at evaluating and comparing classification methods of the PRISMA product. For this purpose, the ASI pre-processed radiance level data (L2D) was used to investigate an area in the Lazio region (Italy). All the classification approaches tested are advanced techniques based on machine learning. Experimentation was carried out using K-nearest neighbors (KNN), Support Vector Machine (SVM) and Random Forest (RF) algorithms, which were compared in terms of accuracy, number of ground control points (GCP) required and ease of implementation. The study showed that the Random Forest approach presents the best compromise among the three proposed approaches, also considering its applicability, which is related to the wider literature and established experimentation in the GIS environment. The research is one of the first studies with the purpose of using the PRISMA data and presents some methodologies for the operational use of this data.

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Combining multispectral imagery and Synthetic Aperture Radar for Detecting Deforestation 26

Combining Multispectral Imagery and Synthetic Aperture Radar...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Reinisch, Elena C. Ziemann, Amanda Flynn, Eric B. Theiler, James Los Alamos Natl Lab Intelligence & Space Res Los Alamos NM 87545 USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

Forest destruction is a main contributor to carbon emissions and loss of biodiversity, making it a matter of global importance. Due to the large global footprint and often inaccessibility of forested areas, remote sensing is one of the most valuable techniques for monitoring deforestation. Spectral imaging is typically favored for material classification of forested areas and identification of broad swaths of deforestation. However, spectral data can fall short in detecting more subtle destruction beneath the forest canopy. Radar remote sensing can help fill this gap, as it has the ability to penetrate through tree canopies such that pixels capture backscatter information from both the canopy and material beneath it. Synthetic aperture radar in particular can capture this information at fine spatial resolution, and techniques such as polarimetry and interferometry can be used to measure biomass and detect deforestation. In this study, we compare synthetic aperture radar data with multispectral data to improve characterization and identification of source signatures captured within a pixel, with specific consideration to detecting areas where thinning is happening beneath the forest canopy. We focus on identifying different types of forest thinning in the Valles Caldera, located in the Jemez Mountains of northern New Mexico. We apply anomalous change detection to a combination of data products derived from multispectral imagery and synthetic aperture radar to determine which combinations are most effective at identifying anomalous features of interest in thinning regions. We find that comparing phase change measured by synthetic aperture radar interferometry to differenced vegetation indices highlights anomalous relationships in the thinning region. When comparing multispectral reflectance to backscatter intensity measured by synthetic aperture radar, the most successful temporal comparisons contained synthetic aperture radar data during the thinning period. This sug

关键词： synthetic aperture radar multispectral imagery anomalous change detection deforestation

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Deep Snow: Synthesizing Remote Sensing imagery with Generative Adversarial Nets 26

Deep Snow: Synthesizing Remote Sensing Imagery with Generati...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Ren, Christopher X. Ziemann, Amanda Theiler, James Durieux, Alice M. S. Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA Descartes Labs Inc 100 N Guadalupe St Santa Fe NM USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in RGB remote sensing imagery, even in an unpaired training setting. We investigate some transformation quality metrics based on deep embedding of the generated and real images which enable visualization and understanding of the training dynamics of the GAN, and provide a useful measure in terms of quantifying how distinguishable the generated images are from real images. We also identify some artifacts introduced by the GAN in the generated images, which are likely to contribute to the differences seen between the real and generated samples in the deep embedding feature space even in cases where the real and generated samples appear perceptually similar.

关键词： Deep Learning Generative Adversarial Networks Machine Learning

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Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages

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GISCIENCE & REMOTE SENSING 2023年第1期60卷 Article: 2170479页

作者： Zhang, Ning Chai, Xiujuan Li, Niwen Zhang, Jianhua Sun, Tan Chinese Acad Agr Sci Agr Informat Inst Beijing Peoples R China Minist Agr & Rural Affairs Key Lab Agr Big Data Beijing Peoples R China Beijing Forestry Univ Forestry Coll Precis Forestry Key Lab Beijing Beijing Peoples R China Swedish Univ Agr Sci Dept Forest Resource Management Umea Sweden

As a quarantine disease with a rapid spread tendency in the context of climate change, accurate detection and location of pine wilt disease (PWD) at different infection stages is critical for maintaining forest health and being highly productivity. In recent years, unmanned aerial vehicle (UAV)-based optical remote-sensing images have provided new instruments for timely and accurate PWD monitoring. Numerous corresponding analysis algorithms have been proposed for UAV-based image classification, but their applicability of detecting different PWD infection stages has not yet been evaluated under a uniform conditions and criteria. This research aims to systematically assess the performance of multi-source images for detecting different PWD infection stages, analyze effective classification algorithms, and further analyze the validity of thermal images for early detection of PWD. In this study, PWD infection was divided into four stages: healthy, chlorosis, red and gray, and UAV-based hyperspectral (HSI), multispectral (MSI), and MSI with a thermal band (MSI&TIR) datasets were used as the data sources. Spectral analysis, support vector machine (SVM), random forest (RF), two- and three-dimensional convolutional network (2D- and 3D-CNN) algorithms were applied to these datasets to compare their classification abilities. The results were as follows: (I) The classification accuracy of the healthy, red, and gray stages using the MSI dataset was close to that obtained when using the MSI&TIR dataset with the same algorithms, whereas the HSI dataset displayed no obvious advantages. (ii) The RF and 3D-CNN algorithms were the most accurate for all datasets (RF: overall accuracy = 94.26%, 3D-CNN: overall accuracy = 93.31%), while the spectral analysis method is also valid for the MSI&TIR dataset. (iiI) Thermal band displayed significant potential in detection of the chlorosis stage, and the MSI&TIR dataset displayed the best performance for detection of all infection stages. Consi

关键词： Pine wilt disease unmanned aerial vehicles optical imagery classification algorithms

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Experiments in Anomalous Change Detection with the Viareggio 2013 Trial Dataset 26

Experiments in Anomalous Change Detection with the Viareggio...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Theiler, James Kucer, Michal Ziemann, Amanda Los Alamos Natl Lab Intelligence & Space Res Los Alamos NM 87545 USA Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

The "Viareggio 2013 Trial" is a hyperspectral dataset obtained from multiple overflights of the Italian city of Viareggio. Careful management of panels and vehicles in the scene enabled the development of valuable ground truth information. One pair of overflights occurred at different times on the same day, and another pair took place over different days. These data were used to compare and evaluate a variety of automated approaches for discovering anomalous changes. Co-registration of the images is acknowledged to be imprecise, so part of the challenge is to identify anomalous changes in a way that is robust to this misregistration. In particular, we employed a local co-registration adjustment (LCRA) algorithm to ameliorate the effects of misregistration;we employed non-maximal suppression (NMS) to take advantage of the discrete nature of the changes;and we used canonical correlation analysis (CCA) to reduce the dimension of our data. We found that, taken together, these improved the performance of the detectors in the low false alarm rate regime of operation.

关键词： anomalous change detection hyperspectral imagery remote sensing local co-registration adjustment non-maximal suppression canonical correlation analysis elliptically-contoured distribution

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A GIS OPEN-SOURCE APPLICATION TO ENHANCE THE IDENTIFICATION OF ARCHAEOLOGICAL CROP MARKS USING REMOTE SENSING DATA 12

A GIS OPEN-SOURCE APPLICATION TO ENHANCE THE IDENTIFICATION ...

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conference on Earth Resources and Environmental Remote Sensing/GIS Applications Xii / SPIE Remote Sensing conference

作者： Duarte, Lia Sanchez, Jesus Garcia Fonte, Joao Teodoro, Ana Claudia Univ Porto FCUP Pole Inst Earth Sci Porto Portugal Univ Porto Fac Sci Dept Geosci Environm & Spatial Planning Porto Portugal CSIC Junta Extremadura Archaeol Inst Merida IAM Merida Spain Univ Exeter Dept Archaeol Exeter Devon England

ISBN: (纸本)9781510645714;9781510645707

The use of vegetation indices to highlight archaeological features from remote sensing (RS) sensors is increasingly in demand, especially due to the possibility of obtaining high-resolution multispectral images with drones. The objective of this work is divided in two steps: i) to test techniques for the semi-automatic identification of crop marks in a study zone, in Salvada, Portugal, and;ii) to develop a Geographical Information Systems (GIS) open-source application, named ArchMarks, under QGIS software, to enhance the identification of archaeological crop marks using RS data/techniques, integrating the automatic creation of indices and the identification techniques tested before. ArchMarks aims to compute several vegetation and soil indices from multispectral imagery considering four bands (Red, Green and Blue and Near InfraRed (NIR)) for enhancing the identification of archaeological crop marks. In order to define the best approach to implement in the plugin, some tests were performed with two algorithms available in QGIS: ContrastEnhancement and WatershedSegmentation. High-resolution aerial imagery (25-50 cm spatial resolution) was obtained from Web Map Service (WMS) services in addition to Sentinel-2 satellite image, whose spatial resolution is of 10 meters in the Red Green Blue-Near InfraRed bands. The application is free and can be adapted to other region of interest. In the future, the plugin will be improved with a methodology to download remote sensing data (aerial images and satellite data) from WMS sources, band stacking, and machine learning algorithms, such as Support Vector Machine (SVM) algorithm, to automatically classify archaeological traces on the vegetation or bare soil.

关键词： crop marks vegetation indices soil marks GIS archaeological features satellite imagery

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Too big, too small, or just right? The influence of multispectral image size on mosquito population predictions in the Greater Toronto Area 26

Too big, too small, or just right? The influence of multispe...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： DeMets, Sydney A. Ziemann, Amanda Manore, Carrie Russell, Curtis Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA Los Alamos Natl Lab Analyt Intelligence & Technol Div Los Alamos NM USA Publ Hlth Ontario Toronto ON Canada

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

Outbreaks of West Nile Virus (WNV) and St. Louis Encephalitis (SLE) are projected to increase in frequency and intensity with climate change, underlining the need to develop better mosquito borne disease (MBD) forecasting systems. Spread by Culex, WNV and SLE have seemingly random spatial and temporal outbreaks, making such outbreaks difficult to predict. However, recent studies have found that mosquito abundance and WNV/SLE transmission are strongly correlated, providing researchers with a foundation for the development MBD forecasting systems. Mosquito populations are impacted by several environmental variables, such as humidity, temperature, vegetation, and available breeding habitat. Mosquito-population forecasting models are beginning incorporate spectral data, such as the normalized difference vegetation index (NDVI). Vegetation is a crucial habitat for some mosquito species, and spectral data offers the best estimate of this habitat virtually anywhere on Earth. Additionally, vegetation offers a proxy for understanding how water flows across a landscape, a crucial consideration in urban areas with high landscape heterogeneity. This research explores how the spatial scale (extent) of multispectral imagery used in mosquito population prediction models influences mosquito population forecasts, specifically in the Greater Toronto Area. We derive three monthly time series of standard spectral indices from multispectral imagery over the Greater Toronto Area from 2004 to 2015;each time series is derived from images taken over the same locations, but using images taken over different spatial footprints. We then explore how spectral indices across the three spatial scales perform as predictors for combined Cx. restuans and Cx. pipiens populations.

关键词： multispectral imagery mosquito population forecasting spectral indices NDVI

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hyperspectral analysis: black inks separation in the Gough map of Great Britain 26

Hyperspectral analysis: black inks separation in the Gough m...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Huang, Sihan Messinger, David W. Bove, Damien Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA Univ London London England

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

The Gough Map is a late medieval map of the island of Great Britain, which has been receiving a lot of research attention and many studies have been conducted to analyze the map. With the hyperspectral image (HSI) of the Gough Map collected, we are allowed to further analysis the map in a different angle. Our goal is to semi-automatically identify and separate iron-gall and carbon black inks written on the Gough map. Unlike traditional target detection problems, there are unique characteristics in our data: Firstly, the targets are not sparsely distributed and their statistical contributions to the background estimation cannot be ignored. Secondly, the spectral differences between the targets are subtle. Lastly, the variances of the background data are so low that the distinction between it and noise is challenging. To address these issues, we made the following modification to the traditional adaptive coherence estimator (ACE): First, manually select the background data instead of using the whole data to estimate the background. Moreover, the pixels with strong spectral features of other pigments are removed and only the black ink candidates are feed into the detector. Thirdly, the number of eigenvectors were limited in the calculation of whitening operator so that the impact of noise can be controlled. It is clear that the trailing eigenvectors are usually dominated by the noise, so the majority of the information of background data can be effectively compressed only with the top-ranked eigenvectors. This is critical because whitening the noise means increasing the variance of the noise based on the fact that our background has super low variances. The whitening matrix needs to be used for whitening target candidates which have subtle spectral differences, and whitening the noise will cause the spectral features to disappear in the whitening sub-space. In conclusion, the whitening operator used in ACE should whiten the useful information of background data and red

关键词： hyperspectral imagery ink identification target detection adaptive coherence estimator(ACE)

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