classificationairborne SARIn recent years, the application of radar polarimetry for remote sensing of land cover types has attracted extensive interest. Numerous microwave scattering models have been developed
For polarization SAR images, the classification map of scattering characteristics is used with speckle filtering of polarimetric synthetic aperture radar (POLSAR) filter =-=[14]-=- for speckle reduction, while perfectly preserving strong point target signatures, and retains edges, linear, and curved ...
(2020) utilised a pre-trained InceptionV3 model for forest fire classification. Using several public forest fire datasets, they adopted k-fold cross-validation and data augmentation strategies to enlarge the data set to facilitate testing of their method. Instead of UAV-based images, Govil et al...
to detect collapsed buildings with high accuracy. The proposed method were tested using synthetic aperture radar data, and Lidar-based digital surface model (DSM). Thus, satellite remote sensing provides an initial estimation for constructing damage maps in wide areas; however, the details of the d...
Gibbs Random Field Models for Model-Based Despeckling of SAR Images Synthetic aperture radar (SAR) images are affected by multiplicative noise called speckle. This noise makes automatic image classification and image interp... E Molina,D. - 《IEEE Geoscience & Remote Sensing Letters》 被引量: 41...
We also distinguish F1-score (higher F1-score better the classification), which is defined as harmonic mean of precision and recall:(36)F1=2PR/(P+R) With a given threshold for the overlap rate, we can also calculate the success rate of the tracker over all frames of the video by count...
Use the facial image acquisition function to extract students' facial features, process each feature through label classification, and then analyze the students' attention and learning emotions. Finally, analyze the effectiveness of the research method application. The results showed that the train_loss...
To predict the approximate location of the small-target pedestrian, a parallel query classification and regression module is added to the AFQ module, which corresponds to the feature mapping accepted by each layer of the AFQ module. The regression and prediction values are passed as location informa...
The SSM based Mamba block has been introduced to model global spatial-spectral features, followed by a fully connected layer to perform binary classification of detected changes. To the best of our knowledge, this is the first to explore using the Mamba and SSM for HCD. Comprehensive experiments...
of samples and features and to improve classification or regression results by integrating the prediction outcomes of these DTs. Conversely, XGBoost is an optimized distributed learning algorithm whose purpose is to enhance prediction performance through serial construction of multiple DT models optimized ...