Multi-Label Classification on Remote-Sensing Images

xiv 1 Chapter 1 1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 What did we do? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Related Work Reviewing previous literature . . . . . . . . . . . . . . . . . . 3

[1]  Hui Xiong,et al.  A Comprehensive Survey on Transfer Learning , 2019, Proceedings of the IEEE.

[2]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[3]  Krzysztof J. Cios,et al.  Review of ensembles of multi-label classifiers: Models, experimental study and prospects , 2018, Inf. Fusion.

[4]  Sashank J. Reddi,et al.  On the Convergence of Adam and Beyond , 2018, ICLR.

[5]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[6]  Lior Bragilevsky,et al.  Deep learning for Amazon satellite image analysis , 2017, 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM).

[7]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[8]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[11]  Genmao Shi.  Use Satellite Data to Track the Human Footprint in the Amazon Rainforest , 2017 .

[12]  Daniel Gardner Stanford,et al.  Multi-label Classification of Satellite Images with Deep Learning , 2017 .

[13]  Abdur Rehman,et al.  Accuracy Based Feature Ranking Metric for Multi-Label Text Classification , 2017 .

[14]  Francisco Charte,et al.  Multilabel Classification: Problem Analysis, Metrics and Techniques , 2016 .

[15]  Mrudul Dixit,et al.  Supervised classification of satellite images , 2016, 2016 Conference on Advances in Signal Processing (CASP).

[16]  Wei Xu,et al.  CNN-RNN: A Unified Framework for Multi-label Image Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[18]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[19]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Shan Suthaharan,et al.  Decision Tree Learning , 2016 .

[22]  Supratik Mukhopadhyay,et al.  DeepSat: a learning framework for satellite imagery , 2015, SIGSPATIAL/GIS.

[23]  Lamberto Ballan,et al.  Love Thy Neighbors: Image Annotation by Exploiting Image Metadata , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[24]  Luisa Verdoliva,et al.  Land Use Classification in Remote Sensing Images by Convolutional Neural Networks , 2015, ArXiv.

[25]  Sunitha Abburu,et al.  Satellite Image Classification Methods and Techniques: A Review , 2015 .

[26]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[27]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[28]  Wei Xu,et al.  Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) , 2014, ICLR.

[29]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[31]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[32]  Shuicheng Yan,et al.  CNN: Single-label to Multi-label , 2014, ArXiv.

[33]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[34]  Johannes Fürnkranz,et al.  Large-Scale Multi-label Text Classification - Revisiting Neural Networks , 2013, ECML/PKDD.

[35]  Zheru Chi,et al.  Multi-instance multi-label image classification: A neural approach , 2013, Neurocomputing.

[36]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[37]  Grigorios Tsoumakas,et al.  Random K-labelsets for Multilabel Classification , 2022 .

[38]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[39]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[40]  Gemma C. Garriga,et al.  Permutation Tests for Studying Classifier Performance , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[41]  Turgay Çelik,et al.  Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering , 2009, IEEE Geoscience and Remote Sensing Letters.

[42]  Trevor Hastie,et al.  Multi-class AdaBoost ∗ , 2009 .

[43]  Tao Mei,et al.  Joint multi-label multi-instance learning for image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Chih-Jen Lin,et al.  A Study on Threshold Selection for Multi-label Classification , 2007 .

[45]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[46]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[47]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[48]  Graeme G. Wilkinson,et al.  Results and implications of a study of fifteen years of satellite image classification experiments , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[49]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[50]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[51]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[52]  J. Friedman.  Stochastic gradient boosting , 2002 .

[53]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[54]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[55]  Thomas G. Dietterich.  Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[56]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[57]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[58]  L. Breiman,et al.  Submodel selection and evaluation in regression. The X-random case , 1992 .

[59]  Horst Bischof,et al.  Multispectral classification of Landsat-images using neural networks , 1992, IEEE Trans. Geosci. Remote. Sens..

[60]  George F. Hepner,et al.  Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification , 1990 .