The accurate identification of walnuts within orchards brings forth a plethora of advantages, profoundly amplifying the efficiency and productivity of walnut orchard management. Nevertheless, the unique characteristics of walnut trees, characterized by their closely resembling shapes, colors, and textures between the walnuts and leaves, present a formidable challenge in precisely distinguishing between them during the annotation process. In this study, we present a novel approach to improve walnut detection efficiency, utilizing YOLOv5 trained on an enriched image set that incorporates both real and synthetic RGB and NIR images. Our analysis comparing results from our original and augmented datasets shows clear improvements in detection when using the synthetic images.
Spectral Synthesis for Satellite-to-Satellite Translation
NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Bay Area Environmental Research Institute（湾区环境研究所）；Microsoft Research AI（微软研究院人工智能）；California State University Monterey Bay（加利福尼亚州立大学蒙特雷湾分校）
Generative Model-Assisted Demosaicing for Cross-multispectral Cameras
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；Image Processing and Interpretation, IMEC Research Group, Ghent University（根特大学IMEC研究组图像处理与解释方向）
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data
University of Cambridge（剑桥大学）；Drexel University（德雷塞尔大学）；Universitat de València（瓦伦西亚大学）；European Space Agency（欧洲空间局）；University of Oxford（牛津大学）；Universidad de Antioquia（安蒂奥基亚大学）
Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI
RISE Research Institutes of Sweden（瑞典RISE研究院）；Luleå University of Technology（吕勒奥理工大学）；Swedish Meteorological and Hydrological Institute（瑞典气象水文研究所）；AI Sweden（瑞典AI中心）；The Swedish Forest Agency（瑞典林业署）
Hyperspectral and multispectral image fusion under spectrally varying spatial blurs -- Application to high dimensional infrared astronomical imaging
University of Toulouse（图卢兹大学）；IRIT/INP-ENSEEIHT（图卢兹计算机研究所/国立图卢兹应用科学学院-国立高等电力技术、电子学、计算机、水力学与电信学院）；CNRS（法国国家科学研究中心）；Institut Universitaire de France (IUF)（法国大学研究院）；ISAE-SUPAERO（法国高等航空航天学院）；Institut de Recherche en Astrophysique et Planétologie (IRAP)（天体物理与行星学研究所）
X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data
German Aerospace Center（德国航空航天中心）；Technical University of Munich（慕尼黑工业大学）；The University of Tokyo（东京大学）；RIKEN（日本理化学研究所）；Wuhan University（武汉大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；INRIA（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
