Bibliographic Details
| Title: |
S-MAT: Semantic-Driven Masked Attention Transformer for Multi-Label Aerial Image Classification |
| Authors: |
Hongjun Wu, Cheng Xu, Hongzhe Liu |
| Source: |
Sensors, Vol 22, Iss 14, p 5433 (2022) |
| Publisher Information: |
MDPI AG, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Chemical technology |
| Subject Terms: |
aerial scene classification, multi-label learning, redundancy removing, label correlation, semantic disentanglement, Chemical technology, TP1-1185 |
| More Details: |
Multi-label aerial scene image classification is a long-standing and challenging research problem in the remote sensing field. As land cover objects usually co-exist in an aerial scene image, modeling label dependencies is a compelling approach to improve the performance. Previous methods generally directly model the label dependencies among all the categories in the target dataset. However, most of the semantic features extracted from an image are relevant to the existing objects, making the dependencies among the nonexistant categories unable to be effectively evaluated. These redundant label dependencies may bring noise and further decrease the performance of classification. To solve this problem, we propose S-MAT, a Semantic-driven Masked Attention Transformer for multi-label aerial scene image classification. S-MAT adopts a Masked Attention Transformer (MAT) to capture the correlations among the label embeddings constructed by a Semantic Disentanglement Module (SDM). Moreover, the proposed masked attention in MAT can filter out the redundant dependencies and enhance the robustness of the model. As a result, the proposed method can explicitly and accurately capture the label dependencies. Therefore, our method achieves CF1s of 89.21%, 90.90%, and 88.31% on three multi-label aerial scene image classification benchmark datasets: UC-Merced Multi-label, AID Multi-label, and MLRSNet, respectively. In addition, extensive ablation studies and empirical analysis are provided to demonstrate the effectiveness of the essential components of our method under different factors. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1424-8220 |
| Relation: |
https://www.mdpi.com/1424-8220/22/14/5433; https://doaj.org/toc/1424-8220 |
| DOI: |
10.3390/s22145433 |
| Access URL: |
https://doaj.org/article/809f7d66afd24d889316ba2b906f327b |
| Accession Number: |
edsdoj.809f7d66afd24d889316ba2b906f327b |
| Database: |
Directory of Open Access Journals |
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