计算机科学
判别式
多光谱图像
遥感
目标检测
人工智能
特征(语言学)
对象(语法)
代表(政治)
探测器
模式识别(心理学)
样品(材料)
计算机视觉
比例(比率)
地质学
地理
哲学
政治
化学
电信
法学
地图学
色谱法
语言学
政治学
作者
Cong Zhang,Kin‐Man Lam,Qi Wang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-17
被引量:17
标识
DOI:10.1109/tgrs.2022.3233881
摘要
Object detection in remote-sensing images is a crucial task in the fields of Earth observation and computer vision. Despite impressive progress in modern remote-sensing object detectors, there are still three challenges to overcome: 1) complex background interference; 2) dense and cluttered arrangement of instances; and 3) large-scale variations. These challenges lead to two key deficiencies, namely, coarse features and coarse samples, which limit the performance of existing object detectors. To address these issues, in this article, a novel coarse-to-fine framework (CoF-Net) is proposed for object detection in remote-sensing imagery. CoF-Net mainly consists of two parallel branches, namely, coarse-to-fine feature adaptation (CoF-FA) and coarse-to-fine sample assignment (CoF-SA), which aim to progressively enhance feature representation and select stronger training samples, respectively. Specifically, CoF-FA smoothly refines the original coarse features into multispectral nonlocal fine features with discriminative spatial–spectral details and semantic relations. Meanwhile, CoF-SA dynamically considers samples from coarse to fine by progressively introducing geometric and classification constraints for sample assignment during training. Comprehensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed method.
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