인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Hyperspectral gas classification plays a critical role in chemical threat detection, industrial safety, and environmental monitoring.
However, conventional RGB imaging is inherently limited for gas discrimination since many gases appear transparent or indistinguishable in the visible spectrum. In this study, fog, phytoncide, and H2O vapor were used to construct gas environments that cannot be reliably differentiated using RGB imagery alone. In contrast, visible near-infrared (VIS - NIR) hyperspectral imaging reveals distinctive spectral signatures that enable practical gas discrimination. To exploit these characteristics, this paper presents HyMixer, a CNN - Transformer hybrid model for VIS - NIR hyperspectral gas classification that jointly learns spatial and spectral representations while prioritizing efficient full-image inference. Experimental results show that VIS - NIR hyperspectral data provide clear separability among gas types. Compared to a strong baseline model, HyMixer achieves substantially improved inference efficiency, with a slight tradeoff in classification accuracy. This study provides a practical foundation for VIS NIR hyperspectral gas identification in time-sensitive scenarios and can be extended to the identification of hazardous gases, including real chemical, biological, and radiological (CBR) agents, in future applications.
However, conventional RGB imaging is inherently limited for gas discrimination since many gases appear transparent or indistinguishable in the visible spectrum. In this study, fog, phytoncide, and H2O vapor were used to construct gas environments that cannot be reliably differentiated using RGB imagery alone. In contrast, visible near-infrared (VIS - NIR) hyperspectral imaging reveals distinctive spectral signatures that enable practical gas discrimination. To exploit these characteristics, this paper presents HyMixer, a CNN - Transformer hybrid model for VIS - NIR hyperspectral gas classification that jointly learns spatial and spectral representations while prioritizing efficient full-image inference. Experimental results show that VIS - NIR hyperspectral data provide clear separability among gas types. Compared to a strong baseline model, HyMixer achieves substantially improved inference efficiency, with a slight tradeoff in classification accuracy. This study provides a practical foundation for VIS NIR hyperspectral gas identification in time-sensitive scenarios and can be extended to the identification of hazardous gases, including real chemical, biological, and radiological (CBR) agents, in future applications.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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