Change Detection in Hyperdimensional Images Using Untrained Models
Deep transfer-learning-based change detection methods are dependent on the availability of sensor-specific pretrained feature extractors. Such feature extractors are not always available due to lack of training data, especially for hyperspectral sensors and other hyperdimensional images. Moreover mo...
Guardado en:
Autores principales: | Sudipan Saha, Lukas Kondmann, Qian Song, Xiao Xiang Zhu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
IEEE
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/53146cd45cf044e3b2d63aa125ce9297 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Patch-Free Bilateral Network for Hyperspectral Image Classification Using Limited Samples
por: Bing Liu, et al.
Publicado: (2021) -
MSMatch: Semisupervised Multispectral Scene Classification With Few Labels
por: Pablo Gomez, et al.
Publicado: (2021) -
A Hybrid Capsule Network for Hyperspectral Image Classification
por: Massoud Khodadadzadeh, et al.
Publicado: (2021) -
An Optimized Deep Neural Network Detecting Small and Narrow Rectangular Objects in Google Earth Images
por: Shenlu Jiang, et al.
Publicado: (2020) -
Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images
por: Fei Ma, et al.
Publicado: (2021)