Wang, Qi <br/><br/>
Learning to Understand Remote Sensing Images 

<br/><br/><br/>English[eng] 
<br/><br/><label>Subjects--Topical Terms: </label><br/>metadata||image classification||sensitivity analysis||ROI detection||residual learning||image alignment||adaptive convolutional kernels||Hough transform||class imbalance||land surface temperature||inundation mapping||multiscale representation||object-based||convolutional neural networks||scene classification||morphological profiles||hyperedge weight estimation||hyperparameter sparse representation||semantic segmentation||vehicle classification||flood||Landsat imagery||target detection||multi-sensor||building damage detection||optimized kernel minimum noise fraction (OKMNF)||sea-land segmentation||nonlinear classification||land use||SAR imagery||anti-noise transfer network||sub-pixel change detection||Radon transform||segmentation||remote sensing image retrieval||TensorFlow||convolutional neural network||particle swarm optimization||optical sensors||machine learning||mixed pixel||optical remotely sensed images||object-based image analysis||very high resolution images||single stream optimization||ship detection||ice concentration||online learning||manifold ranking||dictionary learning||urban surface water extraction||saliency detection||spatial attraction model (SAM)||quality assessment||Fuzzy-GA decision making system||land cover change||multi-view canonical correlation analysis ensemble||land cover||semantic labeling||sparse representation||dimensionality expansion||speckle filters||hyperspectral imagery||fully convolutional network||infrared image||Siamese neural network||Random Forests (RF)||feature matching||color matching||geostationary satellite remote sensing image||change feature analysis||road detection||deep learning||aerial images||image segmentation||aerial image||multi-sensor image matching||HJ-1A/B CCD||endmember extraction||high resolution||multi-scale clustering||heterogeneous domain adaptation||hard classification||regional land cover||hypergraph learning||automatic cluster number determination||dilated convolution||MSER||semi-supervised learning||gate||Synthetic Aperture Radar (SAR)||downscaling||conditional random fields||urban heat island||hyperspectral image||remote sensing image correction||skip connection||ISPRS||spatial distribution||geo-referencing||Support Vector Machine (SVM)||very high resolution (VHR) satellite image||classification||ensemble learning||synthetic aperture radar||conservation||convolutional neural network (CNN)||THEOS||visible light and infrared integrated camera||vehicle localization||structured sparsity||texture analysis||DSFATN||CNN||image registration||UAV||unsupervised classification||SVMs||SAR image||fuzzy neural network||dimensionality reduction||GeoEye-1||feature extraction||sub-pixel||energy distribution optimizing||saliency analysis||deep convolutional neural networks||sparse and low-rank graph||hyperspectral remote sensing||tensor low-rank approximation||optimal transport||SELF||spatiotemporal context learning||Modest AdaBoost||topic modelling||multi-seasonal||Segment-Tree Filtering||locality information||GF-4 PMS||image fusion||wavelet transform||hashing||machine learning techniques||satellite images||climate change||road segmentation||remote sensing||tensor sparse decomposition||Convolutional Neural Network (CNN)||multi-task learning||deep salient feature||speckle||canonical correlation weighted voting||fully convolutional network (FCN)||despeckling||multispectral imagery||ratio images||linear spectral unmixing||hyperspectral image classification||multispectral images||high resolution image||multi-objective||convolution neural network||transfer learning||1-dimensional (1-D)||threshold stability||Landsat||kernel method||phase congruency||subpixel mapping (SPM)||tensor||MODIS||GSHHG database||compressive sensing