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SFRNet: Fine-Grained Oriented Object Recognition via Separate Feature Refinement
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DOI：10.1109/TGRS.2023.3277626.png)
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摘要
En 中文
Fine-grained oriented object recognition (FGO(2)R) is a practical need for intellectually interpreting remote sensing images. It aims at realizing fine-grained classification and precise localization with oriented bounding boxes, simultaneously. Our considerations for the task are general but decisive: 1) the extraction of subtle differences carries a big weight in differentiating fine-grained classes and 2) oriented localization prefers rotation-sensitive features. In this article, we propose a network with separate feature refinement (SFRNet), in which two transformer-based branches are designed to perform function specific feature refinement for fine-grained classification and oriented localization, separately. To highlight the discriminative information advantageous to fine-grained classification, we propose a spatial and channel transformer (SC-Former) to capture both the long-range spatial interactions and the key correlations hidden in the feature channels. Besides, we design a multi region of interest (RoI) loss (MRL) following the protocol of deep metric learning to enhance the separability of finegrained classes further. For oriented localization, we integrate the oriented response convolution with the transformer structure (namely, OR-Former) to assist in encoding rotation information during regression. Extensive experimental results validate the effectiveness and robustness of our SFRNet. Without bells and whistles, our SFRNet achieves the state-of-the-art performance on the large-scale FAIR1M datasets (FAIR1M-1.0 and FAIR1M-2.0). Code will be available at https://github.com/Ranchosky/SFRNet.
Keyword:
Transformers
Location awareness
Remote sensing
Object detection
Feature extraction
Encoding
Detectors
Deep metric learning
fine-grained classification
oriented object detection
vision transformer
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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