Results 31 to 40 of about 46,904 (268)
PACO: Python-Based Atmospheric Correction
Sensors, 2020 The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric ...
Raquel de los Reyes +9 more
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Influence of the Solar Spectra Models on PACO Atmospheric Correction
Remote Sensing, 2022 The solar irradiance is the source of energy used by passive optical remote sensing to measure the ground reflectance and, from there, derive the ground properties.
Raquel De Los Reyes +6 more
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Generating Natural Adversarial Remote Sensing Images [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Over the last years, Remote Sensing Images (RSI) analysis have started resorting to using deep neural networks to solve most of the commonly faced problems, such as detection, land cover classification or segmentation. As far as critical decision making can be based upon the results of RSI analysis, it is important to clearly identify and understand ...
Jean-Christophe Burnel +3 more
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Remote Sensing Upside Down: Exploring the Potential of Ground-Based Multispectral Cameras for Tree Crown Monitoring [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesRecent advancements in remote sensing have enabled increasingly detailed analysis of forest canopies using a range of platforms, from satellites to ground-based systems.
M. Goebel +3 more
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Integrating Crowd-sourced Annotations of Tree Crowns using Markov Random Field and Multispectral Information [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesBenefiting from advancements in algorithms and computing capabilities, supervised deep learning models offer significant advantages in accurately mapping individual tree canopy cover, which is a fundamental component of forestry management.
Q. Mei, J. Steier, D. Iwaszczuk
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REMOTE SENSING IMAGE CLASSIFICATION WITH THE SEN12MS DATASET [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 Abstract. Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene classification in remote sensing. However, in contrast to the computer vision community that has long been using well-established, large-scale standard datasets to
M. Schmitt, M. Schmitt, Y.-L. Wu
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International Journal of Applied Earth Observations and Geoinformation, 2023 Extracting difference features is a key technique for polarimetric synthetic aperture radar (PolSAR) image change detection. Although the current PolSAR change detection algorithms based on convolutional neural networks (CNNs) can capture the local ...
Zhifei Yang +4 more
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GROUND FILTERING OF CO-REGISTERED MOBILE AND STATIONARY LASER SCANS BY USING SUPERPOINTS IN RANSAC PLANES [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 Ground filtering is an important tool for many applications. The high variability of landscapes makes it necessary to perform its computation with 3D points as the only input, that is, with as few as possible algorithm parameters and without any training
D. Stütz +4 more
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Multiscale Classification of Remote Sensing Images [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2012 A huge effort has been applied in image classification to create high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of remote sensing images (RSIs) combined with the traditional image classification challenges made RSI classification a hard task.
Jefersson Alex dos Santos +4 more
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Remote Sensing Image Change Detection With Transformers [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. However, high-resolution remote sensing CD remains challenging due to the complexity of objects in the scene. Objects with the same semantic concept may show distinct spectral characteristics at different times and spatial locations.
Hao Chen 0045 +2 more
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