Name
Title | ||
|---|---|---|
Triplet-Based Semantic Relation Learning for Aerial Remote Sensing Image Change Detection. |
Abstract | ||
|---|---|---|
This letter presents a novel supervised change detection method based on a deep siamese semantic network framework, which is trained by using improved triplet loss function for optical aerial images. The proposed framework can not only extract features directly from image pairs which include multiscale information and are more abstract as well as robust, but also enhance the interclass separabilit... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LGRS.2018.2869608 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Feature extraction,Semantics,Tensile stress,Remote sensing,Training,Optical imaging,Optical sensors | Computer vision,Feature vector,Change detection,Remote sensing,Semantic network,Feature extraction,Aerial image,Semantic relation,Pixel,Artificial intelligence,Mathematics,Semantics | Journal |
Volume | Issue | ISSN |
16 | 2 | 1545-598X |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengya Zhang | 1 | 2 | 0.36 |
| Guangluan Xu | 2 | 14 | 1.07 |
| Keming Chen | 3 | 2 | 0.36 |
| Menglong Yan | 4 | 50 | 8.33 |
| Xian Sun | 5 | 16 | 5.49 |