Zobrazeno 1 - 10
of 481
pro vyhledávání: '"remote sensing image classification"'
Publikováno v:
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, Vol 11, Iss 3 (2024)
The classification of Remote Sensing Images (RSIs) poses a significant challenge due to the presence of clustered ground objects and noisy backgrounds. While many approaches rely on scaling models to enhance accuracy, the deployment of RSI classifier
Externí odkaz:
https://doaj.org/article/ba93bbfad0ce43e99ceffedc63762dfa
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15052-15066 (2024)
In the realm of urban development, the precise classification and identification of land types are crucial for improving land use efficiency. This article proposes a land recognition and classification method based on data sparsity and improved Soft
Externí odkaz:
https://doaj.org/article/1b633eeee3fb49aebf7caed680aaaadf
Autor:
Xiaoli Li, Jinsong Chen, Longlong Zhao, Hongzhong Li, Jin Wang, Luyi Sun, Shanxin Guo, Pan Chen, Xuemei Zhao
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 7621-7639 (2024)
Superpixel segmentation is an essential step of object-oriented remote sensing image classification; the accuracy of the superpixel segmentation boundary will directly affect the classification result. Most of the traditional superpixel segmentation
Externí odkaz:
https://doaj.org/article/63e2200c5fb949428aebb49d911fba79
Autor:
Ye Ziming
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
The countryside is an important part of the social development process, but with the acceleration of urbanization, the protection of rural landscapes as cultural heritage is facing an increasingly severe situation. In this study, image radiation corr
Externí odkaz:
https://doaj.org/article/bad667ea76e74281b7c83504b92e89ab
Publikováno v:
Geo-spatial Information Science, Vol 26, Iss 3, Pp 289-301 (2023)
ABSTRACTHigh Spatial and Spectral Resolution (HSSR) remote-sensing images can provide rich spectral bands and detailed ground information, but there is a relative lack of research on this new type of remote-sensing data. Although there are already so
Externí odkaz:
https://doaj.org/article/06a5fa79af95471392cf252ac0f3cec3
Publikováno v:
Geocarto International, Vol 38, Iss 1 (2023)
In the study of remote sensing image classification, feature extraction and selection is an effective method to distinguish different classification targets. Constructing a high-quality spectral-spatial feature and feature combination has been a wort
Externí odkaz:
https://doaj.org/article/736079c3e9f4440b9d94854f66be3c4b
Publikováno v:
IEEE Access, Vol 11, Pp 114146-114154 (2023)
Since hyperspectral remote sensing images are three-dimensional data cubes with spatial and spectral information, with many wavebands and high inter-band correlation, the number of training samples required for classification is greatly increased. In
Externí odkaz:
https://doaj.org/article/f065256c93b44e7185beacec3aa203a7
Publikováno v:
IEEE Access, Vol 11, Pp 99889-99900 (2023)
With the improvement of remote sensing image resolution, remote sensing image scene classification has become a major difficulty in the research of remote sensing Urban green space spatial layout and site selection. Complex data and network structure
Externí odkaz:
https://doaj.org/article/7e9b11d7ce2e4f659ab9e8a5cda14263
Autor:
Yang Cao, Wei Feng, Yinghui Quan, Wenxing Bao, Gabriel Dauphin, Yijia Song, Aifeng Ren, Mengdao Xing
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 409-418 (2023)
Accurate land use and land cover (LULC) maps are effective tools to help achieve sound urban planning and precision agriculture. As an intelligent optimization technology, genetic algorithm (GA) has been successfully applied to various image classifi
Externí odkaz:
https://doaj.org/article/3e9050ea04b64d73b58adaf44d802a2e
Publikováno v:
Sensors, Vol 24, Iss 4, p 1130 (2024)
Remote sensing image classification (RSIC) is designed to assign specific semantic labels to aerial images, which is significant and fundamental in many applications. In recent years, substantial work has been conducted on RSIC with the help of deep
Externí odkaz:
https://doaj.org/article/42d087a073894dd9a5c68a9300d874aa