Zobrazeno 1 - 10
of 281
pro vyhledávání: '"Lianru Gao"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13966-13980 (2024)
In data acquisition and transmission, hyperspectral images are inevitably corrupted by additive noises, making it challenging to accurately observe and recognize the materials on the surface of the Earth. However, scholars have been addicted to devel
Externí odkaz:
https://doaj.org/article/239dc3e2eef74046bfa588ed17a411b5
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 8, Iss 4, Pp 1258-1273 (2023)
Abstract Most unsupervised or semisupervised hyperspectral anomaly detection (HAD) methods train background reconstruction models in the original spectral domain. However, due to the noise and spatial resolution limitations, there may be a lack of di
Externí odkaz:
https://doaj.org/article/88e24a098f5a46d9ba47f7ebefa1e003
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 3635-3644 (2023)
The attention mechanism is one of the most influential ideas in the deep learning community, which has shown excellent efficiency in various computer vision tasks. Thus, this article proposes the convolution neural network method with the attention m
Externí odkaz:
https://doaj.org/article/108f8546a7ef43a8a98a6a829cf3095b
Autor:
Shuo Wang, Wei Feng, Yinghui Quan, Wenxing Bao, Gabriel Dauphin, Lianru Gao, Xian Zhong, Mengdao Xing
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 5943-5952 (2022)
Hyperspectral images (HSIs) have always played an important role in remote sensing applications. Anomaly detection has become a hot spot in HSI processing in recent years. The popular detecting method is to accurately segment anomalies from the backg
Externí odkaz:
https://doaj.org/article/f051d035b9264f618d8ad42b4cb82dda
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 716-728 (2022)
Hyperspectral images (HSIs) are widely used in various tasks such as earth observation and target detection. However, during the imaging process, HSIs are often corrupted by various noises. In this article, we firstly investigate the advantages of tr
Externí odkaz:
https://doaj.org/article/d5a288cc7a324e138741c98d131f8077
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 1814-1822 (2022)
Due to advances in remote sensing satellite imaging and image processing technologies and their wide applications, intelligent remote sensing satellites are facing an opportunity for rapid development. The key technologies, standards, and laws of int
Externí odkaz:
https://doaj.org/article/dadad3b6f4da4f6196b18927a3be553b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 1926-1940 (2022)
Change detection (CD) is an important application of remote sensing, which provides information about land cover changes on the Earth's surface. Hyperspectral image (HSI) can show more spectral information, which greatly improves the ability of remot
Externí odkaz:
https://doaj.org/article/9ea5585326954476ab57e5385cddb318
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 112, Iss , Pp 102926- (2022)
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity are readily available nowadays, which renders researchers an opportunity to tack
Externí odkaz:
https://doaj.org/article/bc01e1bcfcb94ae7abdf453607438479
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 3988-3999 (2021)
Hyperspectral image (HSI) classification often faces the problem of multiclass imbalance, which is considered to be one of the major challenges in the field of remote sensing. In recent years, deep learning has been successfully applied to the HSI cl
Externí odkaz:
https://doaj.org/article/bccf3cacd3c84d5796629e50b7d835f7
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4101-4114 (2021)
Convolutional neural networks (CNNs) can automatically learn features from the hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features. However, the number of training samples for the classification of HSIs is always l
Externí odkaz:
https://doaj.org/article/111603a4aa184095982f0e18ec5cd01c