.png){kind=icon}
返回
Multiple-Instance Metric Learning Network for Hyperspectral Target Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2023.3291439.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Target detection becomes increasingly important in hyperspectral image (HSI) analysis but is limited by difficulties in acquiring accurate pixel-level training labels. This article proposes a multiple-instance metric learning neural network (MIML-Net) for hyperspectral target detection tasks, which only requires region-level labels and greatly alleviates the laborious pixel-level annotation problems. Our method learns the embeddings of regions with weak labels under attention-based multiple-instance learning (MIL) framework. Based on which, we impose a novel metric-based regularizer to constrain target and background embeddings to two learnable compact clusters with distinct centroids, which further boosts the spectral feature representation ability. The proposed metric-based regularizer enforces a discriminative detector due to its capability to reduce the intraclass variations and encourage the interclass separations simultaneously. Extensive experimental results from both simulated and real-field datasets demonstrate the effectiveness of the proposed MIML-Net in comparison with the state-of-the-art weakly supervised techniques.
Keyword:
Discriminative feature embedding
hyperspetral
metric learning (ML)
multiple-instance learning (MIL)
target detection
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
Measured coral luminescence as a freshwater proxy: comparison with visual indices and a potential age artefact.png)
Coral Reefs
Coral ReefsIF0
PREAI
分享收藏
查看更多内容