Name
Title | ||
|---|---|---|
Feature-Level Change Detection Using Deep Representation and Feature Change Analysis for Multispectral Imagery. |
Abstract | ||
|---|---|---|
Due to the noise interference and redundancy in multispectral images, it is promising to transform the available spectral channels into a suitable feature space for relieving noise and reducing the redundancy. The booming of deep learning provides a flexible tool to learn abstract and invariant features directly from the data in their raw forms. In this letter, we propose an unsupervised change de... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LGRS.2016.2601930 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Feature extraction,Transforms,Redundancy,Lighting,Interference,Machine learning,Principal component analysis | Change detection,Computer science,Remote sensing,Deep belief network,Redundancy (engineering),Artificial intelligence,Deep learning,Computer vision,Feature vector,Pattern recognition,Feature (computer vision),Multispectral image,Feature extraction | Journal |
Volume | Issue | ISSN |
13 | 11 | 1545-598X |
Citations | PageRank | References |
10 | 0.53 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Zhang | 1 | 403 | 71.41 |
| Maoguo Gong | 2 | 2676 | 172.02 |
| Puzhao Zhang | 3 | 65 | 4.55 |
| Linzhi Su | 4 | 190 | 7.35 |
| Jiao Shi | 5 | 10 | 0.53 |