COLLAPSED BUILDING CLASSIFICATION WITH OPTICAL AND SAR DATA BASED ON MANIFOLD LEARNING
The collapse of buildings is a major factor in the casualties and economic losses of earthquake disasters, and the degree of building collapse is an important indicator for disaster assessment. In order to improve the classification of collapsed building coverings (CBC), a new fusion technique was p...
Main Authors: | L. Ding, H. Miao |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2020-08-01
|
Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/83/2020/isprs-annals-V-3-2020-83-2020.pdf |
Similar Items
-
Classification of Polarimetric SAR Images Based on the Riemannian Manifold
by: Yang Wen, et al.
Published: (2017-10-01) -
Learning from the progressive collapse of buildings
by: Giacomo Caredda, et al.
Published: (2023-10-01) -
A STATISTICAL TEXTURE FEATURE FOR BUILDING COLLAPSE INFORMATION EXTRACTION OF SAR IMAGE
by: L. Li, et al.
Published: (2018-04-01) -
Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data
by: Zixuan Wang, et al.
Published: (2023-04-01) -
A multi-manifold learning based instance weighting and under-sampling for imbalanced data classification problems
by: Tayyebe Feizi, et al.
Published: (2023-10-01)