In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similarly to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on mid-wave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
Methane Detection On Board Satellites from Unorthorectified Imagery
University of Oxford（牛津大学）；Delft University of Technology（代尔夫特理工大学）；Universitat de València（瓦伦西亚大学）；University of Surrey（萨里大学）；European Space Agency（欧洲空间局）
Normalize Filters! Classical Wisdom for Deep Vision
Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电子工程与计算机科学系）；Computer Science and Engineering, University of Michigan（密歇根大学计算机科学与工程系）
Aircraft Detection in Satellite Imagery using Deep Learning Object Detectors
Department of Information Technology School of Computational and Physical AB Technologies（信息科技系 计算与物理AB技术学院）；Maulana Abul Kalam Azad University of Sciences Nagercoil, Kanyakumari, Technology, Kristu Jayanti (Deemed to be University) Tamil Nadu, India（马拉那阿布·卡拉姆·阿扎德科学大学纳杰科尔, 古里安, 技术, 克里斯图·贾亚尼大学（被认定为大学）泰米尔纳德邦, 印度）
A Lightweight Global-Target Framework for Multi-Domain No-Reference Image Quality Assessment in UAV Imagery
Department of Mechatronic Engineering, Institute for Basic Sciences, Technology and Innovation, Pan African University (PAUSTI)（机械电子工程系，基础科学、技术与创新研究所，泛非大学）；Department of Mechatronic Engineering, Jomo Kenyatta University of Agriculture and Technology (JKUAT)（机械电子工程系，乔莫·肯雅塔农业与技术大学）；College of Science and Technology, University of Rwanda（科学与技术学院，卢旺达大学）
Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery
School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）；Collaborative Innovation Center for Geospatial Technology（地球空间信息技术协同创新中心）；Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University（武汉大学地理信息系统教育部重点实验室）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Department of Electronics, University of Pavia（帕维亚大学电子系）
Exact Consistency Tests for Gaussian Mixture Filters using Normalized Deviation Squared Statistics
Smead Aerospace Engineering Sciences, University of Colorado Boulder（科罗拉多大学博尔德分校斯米德航空航天工程科学学院）；Aurora Flight Sciences, a Boeing Company（波音公司旗下极光飞行科学公司）
Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning
University of Melbourne（墨尔本大学）；Monash University（莫纳什大学）；Cooperative Research Centre for Water Sensitive Cities（水敏感城市合作研究中心）；Arizona State University（亚利桑那州立大学）
UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery
West Virginia University（西弗吉尼亚大学）；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；The Hong Kong University of Science and Technology（香港科技大学）；Tongji University（同济大学）
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet
University of Padova（帕多瓦大学）；National Centre for Neurology and Psychiatry（国家神经与精神病学中心）；National Inter-University Consortium for Telecommunications (CNIT)（全国大学间电信联盟（CNIT））；University of Coimbra（科英布拉大学）；Polytechnic Institute of Tomar（托马尔理工学院）
System Filter-Based Common Components Modeling for Cross-Subject EEG Decoding
college of Electrical and Information Engineering, Zhengzhou University（郑州大学电气与信息工程学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
