MACHINE LEARNING IN REMOTE SENSING IMAGE ANALYSI

1 Blind Image Deblurring with Joint Extreme Channels and Lo-Regularized Intensity and Gradient Priors 2 Non-Blind Deconvolution with L,-Norm of High-Frequency Fidelity 3 A Novel Framework Method for Non-Blind Deconvolution Using Subspace Images Priors 4 Image Reconstruction with an Edge-Preserving Filtering Prior 5 Compressed Sensing Image with Joint Image-Level and Patch-Level Priors 6 Mixed Noise Removal Based on a Novel Non-Parametric Bayesian Sparse Outlier Model 7 DewaterNet: A Fusion Adversarial Real Underwater Image Enhancement Network 8 Underwater Image Enhancement Using a Multi-Scale Dense Generative Adversarial Network 9 Compressed Sensing Image via a Multi-scale Dilated Residual Convolution Network 10 Attention Guided Global Enhancement and Local Refinement Network for Semantic Segmentation 11 Land Cover Classification Method by SVM and Sentinel-2 Satellite Imagery 12 Optimized Random Forest: A Hyperparameter Tuning Method in Machine Learning Algorithms 13 Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery 14 HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images 15 Efficient Transformer for Remote Sensing Image Segmentation 16 Hyper-LGNet: Coupling Local and Global Feature for Hyperspectral Image Classification 17 Hyper-ES2T: Efficient Spatial-Spectral Transformer for the Classification of Hyperspectral Remote Sensing Images 18 CCTNet: Coupling CNN and Transformer Networks for Crop Segmentation of Remote Sensing Images 19 Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery 20 Efficient DF-UNet: Dual-Flow Architecture for Wheat Yellow Rust Severity Detection by UAV Multispectral Imagery ……