08643nam 2202377z- 450 991036775550332120231214133541.03-03897-699-7(CKB)4100000010106162(oapen)https://directory.doabooks.org/handle/20.500.12854/51489(EXLCZ)99410000001010616220202102d2019 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierLearning to Understand Remote Sensing ImagesVolume 2MDPI - Multidisciplinary Digital Publishing Institute20191 electronic resource (363 pages)3-03897-698-9 With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.metadataimage classificationsensitivity analysisROI detectionresidual learningimage alignmentadaptive convolutional kernelsHough transformclass imbalanceland surface temperatureinundation mappingmultiscale representationobject-basedconvolutional neural networksscene classificationmorphological profileshyperedge weight estimationhyperparameter sparse representationsemantic segmentationvehicle classificationfloodLandsat imagerytarget detectionmulti-sensorbuilding damage detectionoptimized kernel minimum noise fraction (OKMNF)sea-land segmentationnonlinear classificationland useSAR imageryanti-noise transfer networksub-pixel change detectionRadon transformsegmentationremote sensing image retrievalTensorFlowconvolutional neural networkparticle swarm optimizationoptical sensorsmachine learningmixed pixeloptical remotely sensed imagesobject-based image analysisvery high resolution imagessingle stream optimizationship detectionice concentrationonline learningmanifold rankingdictionary learningurban surface water extractionsaliency detectionspatial attraction model (SAM)quality assessmentFuzzy-GA decision making systemland cover changemulti-view canonical correlation analysis ensembleland coversemantic labelingsparse representationdimensionality expansionspeckle filtershyperspectral imageryfully convolutional networkinfrared imageSiamese neural networkRandom Forests (RF)feature matchingcolor matchinggeostationary satellite remote sensing imagechange feature analysisroad detectiondeep learningaerial imagesimage segmentationaerial imagemulti-sensor image matchingHJ-1A/B CCDendmember extractionhigh resolutionmulti-scale clusteringheterogeneous domain adaptationhard classificationregional land coverhypergraph learningautomatic cluster number determinationdilated convolutionMSERsemi-supervised learninggateSynthetic Aperture Radar (SAR)downscalingconditional random fieldsurban heat islandhyperspectral imageremote sensing image correctionskip connectionISPRSspatial distributiongeo-referencingSupport Vector Machine (SVM)very high resolution (VHR) satellite imageclassificationensemble learningsynthetic aperture radarconservationconvolutional neural network (CNN)THEOSvisible light and infrared integrated cameravehicle localizationstructured sparsitytexture analysisDSFATNCNNimage registrationUAVunsupervised classificationSVMsSAR imagefuzzy neural networkdimensionality reductionGeoEye-1feature extractionsub-pixelenergy distribution optimizingsaliency analysisdeep convolutional neural networkssparse and low-rank graphhyperspectral remote sensingtensor low-rank approximationoptimal transportSELFspatiotemporal context learningModest AdaBoosttopic modellingmulti-seasonalSegment-Tree Filteringlocality informationGF-4 PMSimage fusionwavelet transformhashingmachine learning techniquessatellite imagesclimate changeroad segmentationremote sensingtensor sparse decompositionConvolutional Neural Network (CNN)multi-task learningdeep salient featurespecklecanonical correlation weighted votingfully convolutional network (FCN)despecklingmultispectral imageryratio imageslinear spectral unmixinghyperspectral image classificationmultispectral imageshigh resolution imagemulti-objectiveconvolution neural networktransfer learning1-dimensional (1-D)threshold stabilityLandsatkernel methodphase congruencysubpixel mapping (SPM)tensorMODISGSHHG databasecompressive sensingWang Qiauth646598BOOK9910367755503321Learning to Understand Remote Sensing Images3024026UNINA