Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Tianming Zhan"'
Publikováno v:
IET Image Processing, Vol 18, Iss 5, Pp 1373-1384 (2024)
Abstract Deep convolutional neural networks based remote sensing change detection has recently shown significant performance improvement. However, small region changes and global‐local features in high‐resolution remote sensing images are not ful
Externí odkaz:
https://doaj.org/article/9afab5ead2934991a449870d392ef2f5
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 9254-9265 (2022)
Recently, deep convolutional neural network (CNN) hyperspectral change detection methods have achieved significant improvement. However, most CNN hyperspectral change detection methods do not make full use of spectral–spatial feature information. I
Externí odkaz:
https://doaj.org/article/9dfdf19bf4e24b2096355e103fa48b7a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 377-388 (2021)
Change detection (CD) is a hot issue in the research of remote sensing technology. Hyperspectral images (HSIs) greatly promote the development of CD technology because of their high resolution in the spectral domain. However, some traditional CD meth
Externí odkaz:
https://doaj.org/article/ce48051041944536bbd08e1a48cbbbf5
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 9794-9802 (2021)
Change detection is a popular topic in remote sensing that is generally constrained to two remote sensing images captured at two different times. However, the optimal type of remote sensing image for change detection tasks has not yet been determined
Externí odkaz:
https://doaj.org/article/1b1a345b3e8c4e0d8a8d5a7266aaf568
Autor:
Guowei Yang, Shaohua Qi, Teng Yu, Minghua Wan, Zhangjing Yang, Tianming Zhan, Fanlong Zhang, Zhihui Lai
Publikováno v:
IEEE Access, Vol 8, Pp 47914-47924 (2020)
At present, regions of the same class determined by Support Vector Machines (SVM) classifier, Support Vector Domain Description (SVDD) classifier and Deep Learning (DL) classifier may occupy regions of other classes or unknown classes in feature spac
Externí odkaz:
https://doaj.org/article/ed9e3fc7d4904d14986c1016897c7b55
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 1174-1188 (2020)
The low spatial resolution of hyperspectral images leads to the coexistence of multiple ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a hyperspectral image hinders the subsequent analysis and application of
Externí odkaz:
https://doaj.org/article/23e454b949004073992ab8bf31943504
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 4311-4324 (2020)
With the rapid development of aerospace and various remote sensing platforms, the amount of data related to remote sensing is increasing rapidly. To meet the application requirements of remote sensing big data, an increasing number of scholars are co
Externí odkaz:
https://doaj.org/article/ae52a3b3758440c7bd63c65ea041eb44
Publikováno v:
Earth and Space Science, Vol 6, Iss 11, Pp 2214-2226 (2019)
Abstract Correction method can reduce the high deviation between the prediction results of numerical model and the observation results and improve the prediction accuracy. Based on the numerical models, including Rapid Refresh Multi‐scale Analysis
Externí odkaz:
https://doaj.org/article/9dffc0c59aa947c5a6eb5e62d453d0e9
Publikováno v:
IEEE Access, Vol 7, Pp 11868-11881 (2019)
Recently, regression-based classifiers, such as the sparse representation classifier and collaborative representation classifier, have been proposed for hyperspectral image (HSI) classification. However, HSIs are typically corrupted by noise, occlusi
Externí odkaz:
https://doaj.org/article/09efc95f52ff4282ad1c082aece66945
Publikováno v:
IEEE Access, Vol 7, Pp 114714-114720 (2019)
A new image feature extraction method for face recognition called Tri-direction 2D-Fisher Discriminant Analysis (T2D-FDA) is proposed to deal with the Small Sample Size (SSS) problem in conventional 1D-Fisher Discriminant Analysis (1D-FDA). Moreover,
Externí odkaz:
https://doaj.org/article/ff5f24fa13ef41ae889b7a0143255491