Zobrazeno 1 - 10
of 231
pro vyhledávání: '"high spatial resolution remote sensing"'
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTIn the field of remote sensing, using a large amount of labeled image data to supervise the training of fully convolutional networks for the semantic segmentation of images is expensive. However, using a small amount of labeled data can lead
Externí odkaz:
https://doaj.org/article/3d3c51e951a24906a9e9a23ca39e225f
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5580-5593 (2024)
Using object-based deep learning for the urban land cover classification has become a mainstream method. This study proposed an urban land cover classification method based on segments’ object features, deep features, and spatial association featur
Externí odkaz:
https://doaj.org/article/c97a81c957c84d2189357cff7cf68923
Publikováno v:
In Applied Energy 1 July 2024 365
Autor:
Xuejun Guo, Ruisen Zhou
Publikováno v:
IEEE Access, Vol 11, Pp 79232-79239 (2023)
Road extraction from high spatial resolution remote sensing images (HSRRSI) is valuable for thematic mapping, autonomous driving, and natural disaster assessment. Deep neural networks have become a powerful tool for road extraction from HSRRSI. Howev
Externí odkaz:
https://doaj.org/article/2cdc1916a2004c25bf75f6484cbe8476
Publikováno v:
Remote Sensing, Vol 16, Iss 8, p 1357 (2024)
Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolut
Externí odkaz:
https://doaj.org/article/349e31706ca844939e514d25cbae5f95
Publikováno v:
Remote Sensing, Vol 16, Iss 6, p 1061 (2024)
Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolutio
Externí odkaz:
https://doaj.org/article/21cb41214cf44c98ab01b0b178346930
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 7846-7858 (2022)
Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur L
Externí odkaz:
https://doaj.org/article/170d1e40f24b4ca881176f3b2a7f3ebe
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4554 (2023)
Accurate information on the location, shape, and size of photovoltaic (PV) arrays is essential for optimal power system planning and energy system development. In this study, we explore the potential of deep convolutional neural networks (DCNNs) for
Externí odkaz:
https://doaj.org/article/4c4dec73ae0d4d1986df55980a54279e
Publikováno v:
Remote Sensing, Vol 15, Iss 15, p 3755 (2023)
The field of remote sensing information processing places significant research emphasis on object detection (OD) in high-spatial-resolution remote sensing images (HSRIs). The OD task in HSRIs poses additional challenges compared to conventional natur
Externí odkaz:
https://doaj.org/article/0994c235d1f44e26af63568620f64393
Autor:
Wancheng Tao, Yi Dong, Wei Su, Jiayu Li, Fu Xuan, Jianxi Huang, Jianyu Yang, Xuecao Li, Yelu Zeng, Baoguo Li
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
The management of crop residue covering is a vital part of conservation tillage, which protects black soil by reducing soil erosion and increasing soil organic carbon. Accurate and rapid classification of corn residue-covered types is significant for
Externí odkaz:
https://doaj.org/article/209718ea3bf14d92863e1a05fe8c03b6