Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing

Volumn 54, Issue 12, 2016, Pages 7405-7415

  Cheng, Gong ^a   Zhou, Peicheng ^a   Han, Junwei ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Convolutional neural networks (CNNs); machine learning; object detection; remote sensing images; rotation invariant CNN (RICNN)

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; CONVOLUTION; FEATURE EXTRACTION; IMAGE RECONSTRUCTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; OBJECT RECOGNITION; REMOTE SENSING; ROTATION;

COMPREHENSIVE EVALUATION; CONVOLUTIONAL NEURAL NETWORK; FEATURE REPRESENTATION; MULTINOMIAL LOGISTIC REGRESSION; OPTICAL REMOTE SENSING; REMOTE SENSING IMAGES; ROTATION INVARIANT; VERY HIGH RESOLUTION;

OBJECT DETECTION;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; COMPUTER VISION; DATA SET; DECONVOLUTION; DETECTION METHOD; IMAGE ANALYSIS; IMAGE RESOLUTION; MACHINE LEARNING; REGRESSION ANALYSIS; REMOTE SENSING;

EID: 85027047340     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2016.2601622     Document Type: Article

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