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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hafiz Naveed Shahzad | |
dc.date.accessioned | 2022-05-03T09:53:19Z | - |
dc.date.available | 2022-05-03T09:53:19Z | - |
dc.date.issued | 2018 | |
dc.identifier.isbn | 9781789237771 9781789237788 | |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/147548 | - |
dc.language.iso | eng | |
dc.publisher | IntechOpen | |
dc.relation.isbasedon | 10.5772/intechopen.69560 | |
dc.relation.uri | https://www.intechopen.com/books/neoplasm | |
dc.rights.uri | CC BY-NC-ND (姓名標示-非商業性-禁止改作) | |
dc.source | DOAB | |
dc.subject.classification | Neoplasms. Tumors. Oncology | |
dc.subject.other | hyperspectral image classification | |
dc.subject.other | SELF | |
dc.subject.other | SVMs | |
dc.subject.other | Segment-Tree Filtering | |
dc.subject.other | multi-sensor | |
dc.subject.other | change feature analysis | |
dc.subject.other | object-based | |
dc.subject.other | multispectral images | |
dc.subject.other | heterogeneous domain adaptation | |
dc.subject.other | transfer learning | |
dc.subject.other | multi-view canonical correlation analysis ensemble | |
dc.subject.other | semi-supervised learning | |
dc.subject.other | canonical correlation weighted voting | |
dc.subject.other | ensemble learning | |
dc.subject.other | image classification | |
dc.subject.other | spatial attraction model (SAM) | |
dc.subject.other | subpixel mapping (SPM) | |
dc.subject.other | land cover | |
dc.subject.other | mixed pixel | |
dc.subject.other | spatial distribution | |
dc.subject.other | hard classification | |
dc.subject.other | building damage detection | |
dc.subject.other | Fuzzy-GA decision making system | |
dc.subject.other | machine learning techniques | |
dc.subject.other | optical remotely sensed images | |
dc.subject.other | sensitivity analysis | |
dc.subject.other | texture analysis | |
dc.subject.other | quality assessment | |
dc.subject.other | ratio images | |
dc.subject.other | Synthetic Aperture Radar (SAR) | |
dc.subject.other | speckle | |
dc.subject.other | speckle filters | |
dc.subject.other | ice concentration | |
dc.subject.other | SAR imagery | |
dc.subject.other | convolutional neural network | |
dc.subject.other | urban surface water extraction | |
dc.subject.other | threshold stability | |
dc.subject.other | sub-pixel | |
dc.subject.other | linear spectral unmixing | |
dc.subject.other | Landsat imagery | |
dc.subject.other | image registration | |
dc.subject.other | image fusion | |
dc.subject.other | UAV | |
dc.subject.other | metadata | |
dc.subject.other | visible light and infrared integrated camera | |
dc.subject.other | semantic segmentation | |
dc.subject.other | CNN | |
dc.subject.other | deep learning | |
dc.subject.other | ISPRS | |
dc.subject.other | remote sensing | |
dc.subject.other | gate | |
dc.subject.other | hyperspectral image | |
dc.subject.other | sparse and low-rank graph | |
dc.subject.other | tensor | |
dc.subject.other | dimensionality reduction | |
dc.subject.other | semantic labeling | |
dc.subject.other | convolution neural network | |
dc.subject.other | fully convolutional network | |
dc.subject.other | sea-land segmentation | |
dc.subject.other | ship detection | |
dc.subject.other | hyperspectral image | |
dc.subject.other | target detection | |
dc.subject.other | multi-task learning | |
dc.subject.other | sparse representation | |
dc.subject.other | locality information | |
dc.subject.other | remote sensing image correction | |
dc.subject.other | color matching | |
dc.subject.other | optimal transport | |
dc.subject.other | CNN | |
dc.subject.other | very high resolution images | |
dc.subject.other | segmentation | |
dc.subject.other | multi-scale clustering | |
dc.subject.other | vehicle localization | |
dc.subject.other | vehicle classification | |
dc.subject.other | high resolution | |
dc.subject.other | aerial image | |
dc.subject.other | convolutional neural network (CNN) | |
dc.subject.other | class imbalance | |
dc.subject.other | deep learning | |
dc.subject.other | convolutional neural network (CNN) | |
dc.subject.other | fully convolutional network (FCN) | |
dc.subject.other | classification | |
dc.subject.other | remote sensing | |
dc.subject.other | high resolution | |
dc.subject.other | semantic segmentation | |
dc.subject.other | deep convolutional neural networks | |
dc.subject.other | manifold ranking | |
dc.subject.other | single stream optimization | |
dc.subject.other | high resolution image | |
dc.subject.other | feature extraction | |
dc.subject.other | hypergraph learning | |
dc.subject.other | morphological profiles | |
dc.subject.other | hyperedge weight estimation | |
dc.subject.other | semantic labeling | |
dc.subject.other | convolutional neural networks | |
dc.subject.other | remote sensing | |
dc.subject.other | deep learning | |
dc.subject.other | aerial images | |
dc.subject.other | hyperspectral image | |
dc.subject.other | feature extraction | |
dc.subject.other | dimensionality reduction | |
dc.subject.other | optimized kernel minimum noise fraction (OKMNF) | |
dc.subject.other | hyperspectral remote sensing | |
dc.subject.other | endmember extraction | |
dc.subject.other | multi-objective | |
dc.subject.other | particle swarm optimization | |
dc.subject.other | image alignment | |
dc.subject.other | feature matching | |
dc.subject.other | geostationary satellite remote sensing image | |
dc.subject.other | GSHHG database | |
dc.subject.other | Hough transform | |
dc.subject.other | dictionary learning | |
dc.subject.other | road detection | |
dc.subject.other | Radon transform | |
dc.subject.other | geo-referencing | |
dc.subject.other | multi-sensor image matching | |
dc.subject.other | Siamese neural network | |
dc.subject.other | satellite images | |
dc.subject.other | synthetic aperture radar | |
dc.subject.other | inundation mapping | |
dc.subject.other | flood | |
dc.subject.other | optical sensors | |
dc.subject.other | spatiotemporal context learning | |
dc.subject.other | Modest AdaBoost | |
dc.subject.other | HJ-1A/B CCD | |
dc.subject.other | GF-4 PMS | |
dc.subject.other | hyperspectral image classification | |
dc.subject.other | automatic cluster number determination | |
dc.subject.other | adaptive convolutional kernels | |
dc.subject.other | hyperspectral imagery | |
dc.subject.other | 1-dimensional (1-D) | |
dc.subject.other | Convolutional Neural Network (CNN) | |
dc.subject.other | Support Vector Machine (SVM) | |
dc.subject.other | Random Forests (RF) | |
dc.subject.other | machine learning | |
dc.subject.other | deep learning | |
dc.subject.other | TensorFlow | |
dc.subject.other | multi-seasonal | |
dc.subject.other | regional land cover | |
dc.subject.other | saliency analysis | |
dc.subject.other | remote sensing | |
dc.subject.other | ROI detection | |
dc.subject.other | hyperparameter sparse representation | |
dc.subject.other | dictionary learning | |
dc.subject.other | energy distribution optimizing | |
dc.subject.other | multispectral imagery | |
dc.subject.other | nonlinear classification | |
dc.subject.other | kernel method | |
dc.subject.other | dimensionality expansion | |
dc.subject.other | deep convolutional neural networks | |
dc.subject.other | road segmentation | |
dc.subject.other | conditional random fields | |
dc.subject.other | satellite images | |
dc.subject.other | aerial images | |
dc.subject.other | THEOS | |
dc.subject.other | land cover change | |
dc.subject.other | downscaling | |
dc.subject.other | sub-pixel change detection | |
dc.subject.other | machine learning | |
dc.subject.other | MODIS | |
dc.subject.other | Landsat | |
dc.subject.other | very high resolution (VHR) satellite image | |
dc.subject.other | topic modelling | |
dc.subject.other | object-based image analysis | |
dc.subject.other | image segmentation | |
dc.subject.other | unsupervised classification | |
dc.subject.other | multiscale representation | |
dc.subject.other | GeoEye-1 | |
dc.subject.other | wavelet transform | |
dc.subject.other | fuzzy neural network | |
dc.subject.other | remote sensing | |
dc.subject.other | conservation | |
dc.subject.other | urban heat island | |
dc.subject.other | land surface temperature | |
dc.subject.other | climate change | |
dc.subject.other | land use | |
dc.subject.other | land cover | |
dc.subject.other | Landsat | |
dc.subject.other | remote sensing | |
dc.subject.other | SAR image | |
dc.subject.other | despeckling | |
dc.subject.other | dilated convolution | |
dc.subject.other | skip connection | |
dc.subject.other | residual learning | |
dc.subject.other | scene classification | |
dc.subject.other | saliency detection | |
dc.subject.other | deep salient feature | |
dc.subject.other | anti-noise transfer network | |
dc.subject.other | DSFATN | |
dc.subject.other | infrared image | |
dc.subject.other | image registration | |
dc.subject.other | MSER | |
dc.subject.other | phase congruency | |
dc.subject.other | hashing | |
dc.subject.other | remote sensing image retrieval | |
dc.subject.other | online learning | |
dc.subject.other | hyperspectral image | |
dc.subject.other | compressive sensing | |
dc.subject.other | structured sparsity | |
dc.subject.other | tensor sparse decomposition | |
dc.subject.other | tensor low-rank approximation | |
dc.title | Neoplasm | |
dc.type | 電子教科書 | |
dc.classification | 醫學類 | |
Theme: | 教科書-醫學類 |
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