Spectral Discrepancy and Cross-Modal Semantic Consistency Learning for Object Detection in Hyperspectral Images

IF 9.7 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiao He;Chang Tang;Xinwang Liu;Wei Zhang;Zhimin Gao;Chuankun Li;Shaohua Qiu;Jiangfeng Xu
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引用次数: 0

Abstract

Hyperspectral images with high spectral resolution provide new insights into recognizing subtle differences in similar substances. However, object detection in hyperspectral images faces significant challenges in intra- and inter-class similarity due to the spatial differences in hyperspectral inter-bands and unavoidable interferences, e.g., sensor noises and illumination. To alleviate the hyperspectral inter-bands inconsistencies and redundancy, we propose a novel network termed Spectral Discrepancy and Cross-Modal semantic consistency learning (SDCM), which facilitates the extraction of consistent information across a wide range of hyperspectral bands while utilizing the spectral dimension to pinpoint regions of interest. Specifically, we leverage a semantic consistency learning (SCL) module that utilizes inter-band contextual cues to diminish the heterogeneity of information among bands, yielding highly coherent spectral dimension representations. On the other hand, we incorporate a spectral gated generator (SGG) into the framework that filters out the redundant data inherent in hyperspectral information based on the importance of the bands. Then, we design the spectral discrepancy aware (SDA) module to enrich the semantic representation of high-level information by extracting pixel-level spectral features. Extensive experiments on two hyperspectral datasets demonstrate that our proposed method achieves state-of-the-art performance when compared with other ones.
光谱差异和跨模态语义一致性学习在高光谱图像目标检测中的应用
具有高光谱分辨率的高光谱图像为识别相似物质的细微差异提供了新的见解。然而,由于高光谱波段间的空间差异和不可避免的干扰(如传感器噪声和光照),高光谱图像的目标检测在类内和类间相似性方面面临重大挑战。为了减轻高光谱波段间的不一致性和冗余,我们提出了一种新的网络,称为光谱差异和跨模态语义一致性学习(SDCM),该网络可以在广泛的高光谱波段中提取一致的信息,同时利用光谱维数来确定感兴趣的区域。具体而言,我们利用语义一致性学习(SCL)模块,该模块利用带间上下文线索来减少带间信息的异质性,从而产生高度相干的频谱维表示。另一方面,我们在框架中加入了一个光谱门控发生器(SGG),根据波段的重要性过滤掉高光谱信息中固有的冗余数据。然后,我们设计了光谱差异感知(SDA)模块,通过提取像素级光谱特征来丰富高层信息的语义表示。在两个高光谱数据集上的大量实验表明,与其他方法相比,我们提出的方法达到了最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia 工程技术-电信学
CiteScore
11.70
自引率
11.00%
发文量
576
审稿时长
5.5 months
期刊介绍: The IEEE Transactions on Multimedia delves into diverse aspects of multimedia technology and applications, covering circuits, networking, signal processing, systems, software, and systems integration. The scope aligns with the Fields of Interest of the sponsors, ensuring a comprehensive exploration of research in multimedia.
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