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- Neural networks have become integral to remote sensing data processing. Among neural networks, convolutional neural networks (CNNs) in deep learning offer numerous advanced algorithms for object detection in remote sensing imagery, which is pivotal in military and civilian contexts. CNNs excel in extracting features from training samples.
www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2024.1381192/full - People also ask
WEBMar 1, 2021 · Novel remote sensing platforms, such as swarms of microsatellites, or unmanned aerial vehicles (UAV), facilitate a bird’s eye view on vegetation canopies with …
- Author: Teja Kattenborn, Jens Leitloff, Felix Schiefer, Stefan Hinz
- Publish Year: 2021
- Studies of Remote Sensing Convolutional
WEBIn the remote sensing domain, it is crucial to complete semantic segmentation on the raster images, e.g., river, building, forest, etc., on raster images. A deep convolutional …
WEBJan 9, 2023 · Deep learning (DL) solutions and in particular convolutional neural networks (CNN) are now state-of-the-art solution in RS scene classification; however, CNN …
WEBTo this end, we propose a deep-learning-based new framework for multimodal RS data classification, where convolutional neural networks (CNNs) are taken as a backbone …
WEBDec 1, 2021 · This study demonstrates the image analysis capacity of convolutional neural networks (CNNs) to identify the presence of clouds from remote sensing directly, …
WEB1 day ago · Then, local spatial invariant characteristics are extracted from multimodal remote sensing images by convolutional operation. Next, heterogeneous spectral and …
WEBMar 1, 2021 · Remote sensing emerged as a key technology revealing both spatial and temporal vegetation patterns. Harnessing the ever growing streams of remote sensing …
WEBFeb 19, 2019 · A review of Convolutional Neural Networks in Remote Sensing Image. Xinni Liu, Fengrong Han, +2 authors. Yue Zhao. Published in International Conference on… 19 February 2019. …
WEB1 day ago · Eventually, we employ a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node …
Remote Sensing | Free Full-Text | Convolutional …
WEBNov 16, 2019 · Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer …
[2407.03653] reBEN: Refined BigEarthNet Dataset for Remote …
WEB6 days ago · This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for …
WEBmethods like Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in de-tecting significant changes, given two images at different ... In …
Remote Sensing | Free Full-Text | Accuracy Assessment in …
WEBJun 23, 2021 · With origins in the computer vision and image processing communities, the accuracy assessment methods developed for CNN-based DL use a wide range of …
WEBAbstract—Fine-tuning of pretrained convolutional neural net- works (CNNs) has been proven to be an effective strategy for remote sensing image scene classification, …
Fully convolutional neural networks for remote sensing image ...
WEBWe propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for …
A Mamba-based Siamese Network for Remote Sensing Change …
WEB1 day ago · Change detection in remote sensing images is an essential tool for analyzing a region at different times. It finds varied applications in monitoring environmental …
Enhancing class-incremental object detection in remote sensing …
WEB1 day ago · AbstractObject detection plays a important role within the field of remote sensing, boasting significant applications including intelligent monitoring and urban …
Triple disentangled network with dual attention for remote sensing ...
WEBJul 2, 2024 · Remote sensing applications, such as detection and recognition of objects, need a large number of high spatial resolution (HR) images. As a feasible technique, …
Multi-scale Convolutional Neural Network for Remote Sensing …
WEBJun 25, 2020 · With the widely used of deeplearning technology, Convolutional Neural Network was used to detect remote sensing change. FC-Siam-conc and FC-siam-diff is …
Deep Complex Convolutional Neural Networks for Remote …
WEBOct 19, 2022 · This paper mainly studies and analyzes deep complex neural network and complex residual neural network, summarizes and analyzes from the two, and finally …
Improved convolutional neural network in remote sensing image ...
WEBApr 25, 2020 · In this study, the algorithm is improved on the basis of convolutional neural network, and experiments are carried out on multi-source remote sensing images with …
Remote sensing image compression with long-range convolution …
WEBAug 1, 2023 · Long-range convolution. •. Long-range context information can help the model capture spatial redundant features. •. Surpasses the most deep learning-based methods …
Remote Sensing Image Registration Using Convolutional Neural …
WEBJan 3, 2018 · Image features extracted by a convolutional neural network (CNN) have achieved the state-of-the-art performance for image classification and retrieval problems, …
Remote Sensing | Free Full-Text | Pansharpening by …
WEB1. Introduction. Multi-resolution images are widespread in remote sensing as they provide users with images at the highest resolution both in the spatial and in the spectral domains.
Automated Rooftop Solar Panel Detection Through Convolutional …
WEB6 days ago · DOI: 10.1080/07038992.2024.2363236 Corpus ID: 270979878; Automated Rooftop Solar Panel Detection Through Convolutional Neural Networks …
Convolutional neural networks for satellite remote sensing at …
WEBSep 15, 2021 · Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval
Efficient and lightweight convolutional neural network architecture ...
WEBDOI: 10.1016/j.patcog.2024.110752 Corpus ID: 271042677; Efficient and lightweight convolutional neural network architecture search methods for object classification …
Remote Sensing Scene Classification Using Convolutional …
WEBMay 8, 2019 · High-resolution remote sensing scene classification (HR-RSSC) plays an increasingly important role since it aims to enhance the scene semantic understanding. Re.
Remote Sensing | Free Full-Text | Fully Convolutional Neural …
WEBWe design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) …
Rethinking Remote Sensing Change Detection With A Mask View
WEBJun 21, 2024 · Remote sensing change detection aims to compare two or more images recorded for the same area but taken at different time stamps to quantitatively and …
Remote Sensing Image Fusion With Deep Convolutional Neural …
WEBIn this study, based on the deep convolutional neural network, a remote sensing image fusion method that can adequately extract spectral and spatial features from source …
Remote Sensing Report Spotlights Work at USGS EROS
WEB1 day ago · The report, released online, is made up of brief summaries of work being done by the various DOI agencies using satellite imagery, aerial photos and data from drones …
Remote Sensing | An Open Access Journal from MDPI
WEBAug 5, 2011 · Remote Sensing is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published …
Remote Sensing | Free Full-Text | Polar Sea Ice Monitoring Using …
WEB3 days ago · The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and …
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