The current study investigates the application of the photogrammetric technique, commonly used for satellite measurements, on the All Sky imager (ASI), which is used for night airglow observations from Kolhapur (16.8 ...
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The current study investigates the application of the photogrammetric technique, commonly used for satellite measurements, on the All Sky imager (ASI), which is used for night airglow observations from Kolhapur (16.8 degrees N, 74.2 degrees E), India. Using imageprocessing methods, the current study spanning from the year 2016 to 2020 (March - May months) estimates the cloud speeds to range from 10 m s(-1) to 18 m s(-1) during the period under consideration. The slowest speed of 10 +/- 3 m s(-1) was evidenced in 2017, while it takes value of 15 +/- 3 m s(-1) over other years. The clouds are found to move in a south-westerly direction during the time period under consideration. The cloud cover fraction varies in between similar to 0.178 to 0.594 from 2016 to 2020. Our analysis indicates systematic changes in the pre-monsoon cloud fraction and direction of the cloud movement. The observed trend indicates that the monsoon pattern is somewhat changing
Imaging of static scenes under water is widely used in underwater detection technology. Aiming at the problem of severe distortion of underwater scene images caused by water surface fluctuations. An underwater distort...
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With the evolution of satellite video technology, the domain has garnered increasing attention. Concurrently, advancements in deep learning have yielded numerous outcomes in target detection. This paper introduces a n...
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Big advancements in the field of Deep Learning allow the automated extraction of glacier calving fronts from satellite imagery. However, current efforts on Synthetic Aperture Radar (SAR) imagery still produce coarse a...
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ISBN:
(纸本)9798350320107
Big advancements in the field of Deep Learning allow the automated extraction of glacier calving fronts from satellite imagery. However, current efforts on Synthetic Aperture Radar (SAR) imagery still produce coarse and partly spurious predictions. Therefore, in this study, a two-dimensional fully-connected Conditional Random Field (CRF) is incorporated into the post-processing of a deep learning-based calving front delineation pipeline. The CRF takes as input the artificial neural network prediction and the original SAR image. Experiments are undertaken using the newly introduced benchmark dataset CaFFe and results are compared to the associated baseline. By introducing the CRF into the post-processing of the baseline's pipeline, the mean distance error of the front prediction is improved by an average of 27 meters. The code is available at https://***/EntChanelt/GlacierCRF.
The quality of land use maps often refers to the data quality, but distributional uncertainty between training and test data must also be considered. In order to address this uncertainty, we follow the strategy to det...
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ISBN:
(纸本)9783031546044;9783031546051
The quality of land use maps often refers to the data quality, but distributional uncertainty between training and test data must also be considered. In order to address this uncertainty, we follow the strategy to detect out-of-distribution samples using uncertainty maps. Then, we use supervised machine learning to identify those samples. For the investigations, we use an uncertainty metric adapted from depth maps fusion and Monte-Carlo dropout based predicted probabilities. The results show a correlation between out-of-distribution samples, misclassifications and uncertainty. Thus, on the one hand, out-of-distribution samples are identifiable through uncertainty, on the other hand it is difficult to distinguish between misclassification, anomalies and out-of-distribution.
Curbing the introduction, spread, and impact of invasive species remains a longstanding management and policy prerogative. In recent decades, globalization and environmental change have further complicated efforts to ...
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Curbing the introduction, spread, and impact of invasive species remains a longstanding management and policy prerogative. In recent decades, globalization and environmental change have further complicated efforts to execute science-based actions that address these challenges. New technologies offer exciting opportunities to advance invasion science knowledge, enhance management actions, and guide policy strategies but are increasingly complex and inaccessible to most practitioners. In the present article, we offer a synthetic perspective of innovative technologies with applications for invasive species management related to pathway intervention, spread prevention, impact mitigation, and public engagement. We also describe tools that augment big data processing required by some methods (e.g., remotesensing, mobile application data), such as automated image and text recognition built on machine learning. Finally, we explore challenges and opportunities for successful integration of emerging technologies into invasive species management, focusing on pipelines that enable practitioners to integrate tools into practice while recognizing logistic and financial constraints.
The proceedings contain 114 papers. The topics discussed include: research on intelligent English-Chinese translation proofreading system based on gated feedback re current neural networks;intelligent recognition of s...
ISBN:
(纸本)9798350374407
The proceedings contain 114 papers. The topics discussed include: research on intelligent English-Chinese translation proofreading system based on gated feedback re current neural networks;intelligent recognition of structures in earth and rock dam images based on MASK-RCNN;a pupil diameter measurement system based on imageprocessing;image enhancement and deep learning in predicting the Gleason score of transcrectal ultrasound images of prostate cancer;research on video logo removal processing method based on MATLAB;analysis and evaluation of quality control throughout production of real scene 3D modeling based on oblique aerial photography;improving remotesensingimage classification through stochastic bilevel optimization;cross-domain image translation algorithm based on self-cross auto-encoder;a fast image mosaic algorithm based on feature partition extraction;and video compression and action recognition in self-supervised learning.
Classification of Hyperspectral remotesensingimage (HSI) is a key technology for fine feature information extraction and recognition. Aiming at the problems of the existing HSI classification network architecture be...
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The proceedings contain 19 papers. The topics discussed include: enhancing cyberattack detection in IoT environments through advanced resampling techniques;segmentation of hepatocytes nuclei using YOLO and mathematica...
ISBN:
(纸本)9798350391886
The proceedings contain 19 papers. The topics discussed include: enhancing cyberattack detection in IoT environments through advanced resampling techniques;segmentation of hepatocytes nuclei using YOLO and mathematical morphology;music genre classification using contrastive dissimilarity;a generative approach to electrical impedance tomography image reconstruction using prior information;on the possibilities of using traditional patternrecognition techniques in Brazilian banknote characterization;neural architecture search for enhancing action video recognition in compressed domains;denoising autoencoder for partial discharge identification in instrument transformers;and immersive virtual conference room: bridging the gap between remote collaboration and physical presence.
remote operating and autonomous systems are widely applied in various fields, and the development of technology for human machine interface and communication is strongly demanded. In order to overcome the limitations ...
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ISBN:
(纸本)9781510655386;9781510655379
remote operating and autonomous systems are widely applied in various fields, and the development of technology for human machine interface and communication is strongly demanded. In order to overcome the limitations of the conventional keyboard and tablet devices, various vision sensors and state-of-the-art artificial intelligence imageprocessing techniques are used to recognize hand gestures. In this study, we propose a method for recognizing a reference sign language using auto labeled AI model training datasets. This study can be applied to the remote control interfaces for drivers to vehicles, person to home appliances, and garners to entertainment contents and remote character input technology for the metaverse environment.
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