High-resolution remote sensing images

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August undefined, 2024

WebAug 16, 2024 · We present a deep learning-based framework for individual tree crown delineation in aerial and satellite images. This is an important task, e.g., for forest yield or carbon stock estimation. In contrast to earlier work, the presented method creates irregular polygons instead of bounding boxes and also provides a tree cover mask for areas that … WebThe rapid development of remote sensing sensors allows diverse access to very high-resolution (VHR) remote sensing images. A pixel-based land cover classification, also known as semantic segmentation, using very high spatial resolution images has significant application value in land resource management [1,2], urban planning [3,4], change …

Pansharpening for Cloud-Contaminated Very High-Resolution Remote …

WebFeb 1, 2024 · In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information … WebMay 15, 2024 · Semantic labeling for high resolution aerial images is a fundamental and necessary task in remote sensing image analysis. It is widely used in land-use surveys, change detection, and environmental protection. Recent researches reveal the superiority of Convolutional Neural Networks (CNNs) in this task. However, multi-scale object … small red spots on hand

A deeply supervised image fusion network for change detection in …

WebNov 16, 2024 · However, automatic building extraction from high spatial resolution remote sensing images has been a challenging task due to the various building shapes and colors, imaging conditions, and complex background objects. Current methods in building extraction are generally based on deep convolution networks, and they mostly use an … WebDec 22, 2024 · In this paper, a deeply supervised attentive high-resolution network (DSAHRNet) is proposed for remote sensing image change detection. First, we design a spatial-channel attention module to decode change information from bitemporal features. The attention module is able to model spatial-wise and channel-wise contexts. WebMar 25, 2024 · With the aim of automatically extracting fine change information from ground objects, change detection (CD) for very high resolution (VHR) remote sensing images is extremely essential in various applications. However, the increase in spatial resolution, more complicated interactive relationships of ground objects, more evident diversity of spectra, … highly cited chinese researchers 2021

High-Resolution Remote Sensing Image Semantic …

Unsupervised Change Detection for VHR Remote Sensing Images …

WebJul 10, 2024 · High spatial resolution remote sensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more details through high and very high resolution images (10 to 100 cm/pixel). This unprecedented level of detail offers the … WebDec 1, 2024 · Abstract: Remote sensing images are primary data sources for land use classification. High spatial resolution images enable more accurate analysis and identification of land cover types. However, a higher spatial resolution also brings new challenges to the existing classification methods. highly chargedWebA deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 183-200. License Code and datasets are released for non-commercial and research purposes only. For commercial purposes, please contact the authors. small red spots on inside of cheek

"WebFeb 24, 2024 · Content-based remote sensing image retrieval (RSIR), which uses image feature to efficiently and rapidly retrieve interested images from a large-scale dataset (Li et al. 2024; Ye et al. 2024 ), can be used to solve this problem. " - High-resolution remote sensing images

High-resolution remote sensing images

Spatial Resolution In Remote Sensing: Which One To Choose?

WebMar 9, 2024 · A semantic understanding of high spatial resolution remote sensing images using directional geospatial relationships is proposed in this paper. Different kinds of … WebTo overcome this problem, a fine-grained, structured attention-based method is proposed to utilize the structural characteristics of semantic contents in high-resolution remote …

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WebJun 2, 2024 · High-resolution remote sensing images usually contain complex semantic information and confusing targets, so their semantic segmentation is an important and challenging task. To resolve the problem of inadequate utilization of multilayer features by existing methods, a semantic segmentation method for remote sensing images based on … WebSemi-supervised methods have made remarkable achievements via utilizing unlabeled samples for optical high-resolution remote sensing scene classification. However, the labeled data cannot be effectively combined with unlabeled data in the existing semi-supervised methods during model training. To address this issue, we present a semi …

WebFeb 1, 2024 · In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts. … WebFeb 17, 2024 · This study proposed a new deep learning-based framework for extracting tailings pond margins from high spatial resolution (HSR) remote sensing images by combining You Only Look Once (YOLO) v4 and the random forest algorithm. At the same time, we created an open source tailings pond dataset based on HSR remote sensing …

WebMar 25, 2024 · With the aim of automatically extracting fine change information from ground objects, change detection (CD) for very high resolution (VHR) remote sensing images is … WebAccess high-resolution aerial imagery and geospatial data products from over 25 countries, including oblique, true ortho, DSM, multispectral, and property analytics. Look beyond images and their immediate data outputs. Give anyone—decision … The images they continue to gather provide an increasingly powerful tool for …

WebDec 23, 2024 · Considering the fragmentation of urban forests, three different resolutions of remote images, i.e., MODIS, Landsat and Sentinel-2, were used to evaluate the ability to … highly charged traumaticWebJun 3, 2024 · Abstract: Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost of acquisition equipment, thereby providing a feasible way to improve the quality of remote sensing images. Clearly, image SR is a severe ill-posed problem. With the development of deep learning, the … highly cited paper esiWebDec 22, 2024 · The high-resolution 0.4m/px image from Kompsat-3A lets you clearly see buildings, roads, and even cars, but in most cases you have to pay for that level of detail. … highly cited articlesWebJul 28, 2024 · High-resolution remote sensing (HRS) image analysis is a fundamental but challenging problem. To bridge the semantic gap, scene understanding has been proposed to achieve higher-level interpretation, through classifying the HRS scene through spatial relationship cognition and semantic induction between the land-cover objects. As a new … small red spots on penisWebAug 1, 2024 · With the development of high resolution optical sensors (e.g., WorldView-3, GeoEys-1, QuickBird, and Gaofen-2), the increasing availability of high resolution remote sensing images has widened the range of potential applications of change detection in high resolution bi-temporal images. small red spots on legs itchyWebMay 28, 2024 · Based on high-resolution images, this study used concentric buffer zones to explore the characteristics and relationship between landscape pattern indexes (LPIs) and … highly cited papers esiWebScene classification of high spatial resolution (HSR) images can provide data support for many practical applications, such as land planning and utilization, and it has been a crucial research topic in the remote sensing (RS) community. Recently, deep learning methods driven by massive data show the impressive ability of feature learning in the field of HSR … highly cited chinese researchers 2020 scopus