Name
Abstract | ||
|---|---|---|
Successful remote sensing image registration is an important step for many remote sensing applications. The scale-invariant feature transform (SIFT) is a well-known method for remote sensing image registration, with many variants of SIFT proposed. However, it only uses local low-level information, and loses much middle- or high-level information to register. Image features extracted by a convoluti... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LGRS.2017.2781741 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Feature extraction,Remote sensing,Image registration,Registers,Robustness,Transforms | Scale-invariant feature transform,Computer vision,Convolutional neural network,Feature (computer vision),Remote sensing,Feature extraction,Remote sensing application,Robustness (computer science),Artificial intelligence,Contextual image classification,Image registration,Mathematics | Journal |
Volume | Issue | ISSN |
15 | 2 | 1545-598X |
Citations | PageRank | References |
5 | 0.40 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Famao Ye | 1 | 10 | 1.14 |
| Yanfei Su | 2 | 5 | 0.40 |
| Hui Xiao | 3 | 29 | 6.96 |
| Xuqing Zhao | 4 | 8 | 0.77 |
| Weidong Min | 5 | 40 | 9.44 |