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- High-resolution remote sensing images123:
- Are obtained from remotely sensed sensors and platforms.
- Include optical imagery, SAR, and LiDAR.
- Reveal finer details such as individual trees, vehicles, buildings, and more2.
- Are widely available, opening new opportunities for geo-related applications1.
- Are characterized by spatial features of large spatial scales across multiple directions, which are crucial for dense prediction tasks such as semantic segmentation and change detection3.
Learn more:✕This summary was generated using AI based on multiple online sources. To view the original source information, use the "Learn more" links.With the rapid development of remotely sensed sensors and platforms, multi-source high resolution remote sensing images (e.g., optical imagery, SAR, and LiDAR) are widely available, opening new opportunities for geo-related applications.www.mdpi.com/journal/remotesensing/special_issu…High-resolution remote sensing photography has the apparent benefit of revealing finer details such as individual trees, vehicles, buildings, and more. In EOSDA LandViewer, you may choose from eight data sets from satellites with the highest spatial resolutions, ranging from 5 meters (SPOT 5) to 40 centimeters (Kompsat-3A).eos.com/blog/spatial-resolution/VHR remote sensing images are characterized by spatial features of large spatial scales across multiple directions, which are crucial for dense prediction tasks such as semantic segmentation and change detection.arxiv.org/html/2404.02668v1 - People also ask
An improved semantic segmentation algorithm for high-resolution …
WEBApr 27, 2024 · High-resolution remote sensing images can give rich feature information and finely present the spatial structure and textural features due to their complex and …
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Research on Instance Segmentation of High-Resolution Remote …
WEBThis paper presents an overview of the current problems and challenges associated with remote sensing image instance segmentation, as well as future trends and developments.
Extraction of water bodies from high-resolution remote sensing …
WEB4 days ago · This methodology first creates a comprehensive dataset of high-resolution remote sensing images, then progresses through the Xception baseline network for …
Spatial Resolution In Remote Sensing: Which One To Choose?
- The biggest benefit of high spatial resolution vs. low spatial resolution is the precise level of detail it shows. Additionally, high-resolution remote sensing provides coverage whenever and wherever it is needed. However, higher-quality images are typically more expensive and less available to customers. Another drawback of high resolution remote ...
- Occupation: Project
- Gender: Female
- Works For: EOS Data Analytics
- Published: Dec 22, 2022
Recent Advances in High Resolution Remote Sensing Image …
WEBNov 15, 2023 · With the rapid development of remotely sensed sensors and platforms, multi-source high resolution remote sensing images (e.g., optical imagery, SAR, and …
High spatial resolution remote sensing image segmentation
WEBJan 3, 2021 · High-resolution remote sensing images can provide many surface features, such as shape, texture, size and other features, with a richer spatial information that can …
High Resolution Remote Sensing Image Classification Based on …
WEBIn this paper, deep transfer learning is the core technology of remote sensing image classification model, and VGG16, Inception V3, ResNet50 and MobileNet are used to …
Joint superpixel and Transformer for high resolution remote …
WEBMar 1, 2024 · Deep neural networks combined with superpixel segmentation have proven to be superior to high-resolution remote sensing image (HRI) classification.
Geographic Scene Understanding of High-Spatial …
WEBJun 13, 2022 · Based on the basic connotation of geographic scene understanding of high-spatial-resolution remote sensing images, this paper firstly summarizes the keystones in geographic scene …
Multiscale Global Context Network for Semantic Segmentation of …
WEBSemantic segmentation of high-resolution remote sensing images (HRSIs) is a challenging task because objects in HRSIs usually have great scale variance and appe.
Single-Temporal Supervised Learning for Universal Remote …
WEBJun 20, 2024 · Change detection with multi-temporal high-resolution remote sensing imagery is a fundamental Earth vision task, which aims to provide prompt and accurate …
Remote Sensing | Free Full-Text | Global-Local Collaborative …
WEB2 days ago · However, the analysis of dual-temporal change detection (CD) remains a challenge in high-resolution optical remote sensing images due to the complexities in …
Semantic understanding of high spatial resolution remote sensing …
WEBMar 9, 2023 · A semantic understanding of high spatial resolution remote sensing images using directional geospatial relationships is proposed in this paper. Different kinds of …
A comprehensive review on deep learning based remote sensing …
WEBSep 1, 2022 · Single Remote Sensing Image Super Resolution (SRSISR) provides effective solutions to increase the spatial and/or spectral resolution of low-resolution …
A deeply supervised image fusion network for change detection in …
WEBAug 1, 2020 · In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, …
Self-supervised learning for remote sensing scene ... - Nature
WEBJan 9, 2023 · An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3+.
Road Extraction Methods in High-Resolution Remote Sensing …
WEBAbstract: Road extraction from high-resolution remote sensing images is a challenging but hot research topic in the past decades. A large number of methods are invented to …
Using high-resolution remote sensing images to explore the …
WEBMay 28, 2021 · Based on high-resolution images, this study used concentric buffer zones to explore the characteristics and relationship between landscape pattern indexes (LPIs) …
Land-Cover Classification with High-Resolution Remote Sensing …
WEBJul 16, 2018 · In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex …
Land-cover classification with high-resolution remote sensing …
WEBFeb 1, 2020 · To our knowledge, GID is the first and largest well-annotated land-cover classification dataset with high-resolution remote sensing images up to 4 m. It can …
Automatic registration of urban high-resolution remote sensing …
WEBAug 24, 2022 · Automatic registration of high-resolution remote sensing images (HRRSIs) has always been a severe challenge due to the local deformation caused by …
Deep semantic understanding of high resolution remote sensing …
WEBAug 18, 2016 · Hence, a deep multimodal neural network model for semantic understanding of the high resolution remote sensing images is proposed in this paper, which uses …
Vectorized building extraction from high-resolution remote …
WEBVectorized building extraction from high-resolution remote sensing images using spatial cognitive graph convolution model Du, Zhuotong; ... ISPRS Journal of Photogrammetry …
Remote Sensing | Free Full-Text | High-Resolution Remote …
WEBJul 14, 2017 · In this paper, we propose that more powerful features for high-resolution remote sensing image representations can be learned using only several tens of …
SOENet: a multi-resolution network for sheep extraction from high ...
WEB2 days ago · It is a typical grassland area with flat terrain and numerous lakes. High spatial resolution (0.5 m) remote sensing images taken by the Beijing-3 satellite were …
High-Resolution Remote Sensing Bitemporal Image Change …
WEBWith the development of remote sensing technology, high-resolution (HR) remote sensing optical images have gradually become the main source of change detection.
Semantic Descriptions of High-Resolution Remote Sensing …
WEBSemantic Descriptions of High-Resolution Remote Sensing Images Abstract: Image captioning has attracted more and more attention in remote sensing filed since it …
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