Name
Title | ||
|---|---|---|
Dilated Residual Network Based On Dual Expectation Maximization Attention For Semantic Segmentation Of Remote Sensing Images |
Abstract | ||
|---|---|---|
Compared with common RGB images, remote sensing images (RSIs) have larger size and lower spatial resolution. RSIs are usually cropped into sub-images for training convolutional neural networks (CNNs), which loses amounts of context information, thus limiting the extraction of feature interdependencies and reducing the accuracy of semantic segmentation. In this paper, a novel dilated residual network based on dual expectation maximization attention (DE-MANet) is proposed for semantic segmentation of RSIs. In specific, we append a dual expectation maximization attention (DEMA) module on top of the dilated CNN. The spatial expectation maximization attention (SEMA) can model spatial feature interdependencies to acquire rich long-range contextual information. The channel expectation maximization attention (CEMA) enhances discriminant ability of channel-wise feature representations through extracting the channel dependencies. We evaluate the model on the dataset released in the Tianzhi Cup Artificial Intelligence Challenge and achieve 85.60% pixel accuracy and 69.00% mean intersection over union (mIoU). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9324423 | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | DocType | Citations |
Remote Sensing Images, Semantic Segmentation, DEMANet, Dual Expectation Maximization Attention | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiachao Liu | 1 | 0 | 0.34 |
| Xinyue Xiong | 2 | 0 | 0.34 |
| Jiaojiao Li | 3 | 3 | 6.16 |
| Chaoxiong Wu | 4 | 2 | 3.06 |
| Rui Song | 5 | 40 | 7.79 |