Results 111 to 120 of about 2,397 (207)
PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network
Remote SensingPolarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain
Mengxuan Zhang +6 more
doaj +1 more source
Visualization of Skewed Data: A Tool in R [PDF]
, 2014 In this work we present a visualization tool specifically tailored to deal with skewed data. The technique is based upon the use of two types of notched boxplots (the usual one, and one which is tuned for the skewness of the data), the violin plot, the ...
Frery, A. C. +2 more
core +2 more sources
Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information [PDF]
, 2017 Xianyuan Wang +3 more
openalex +1 more source
SCHATTEN MATRIX NORM BASED POLARIMETRIC SAR DATA REGULARIZATION. APPLICATION OVER CHAMONIX MONT-BLANC [PDF]
, 2013 International audienceThe paper addresses the filtering of Polarimetry Synthetic Aperture Radar (PolSAR) images. The filtering strategy is based on a regularizing cost function associated with matrix norms called the Schatten p-norms.
Atto, Abdourrahmane +2 more
core +1 more source
PolSAR Image Classification by Introducing POA and HA Variances [PDF]
, 2023 Zeying Lan, Yang Liu, Jianhua He, Xin Hu
openalex +1 more source
A 3-D Convolutional Vision Transformer for PolSAR Image Classification and Change Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe scattering properties of targets in polarimetric synthetic aperture radar (PolSAR) images are directly influenced by the targets' orientations, as the scattering properties from the same target with different orientations can be very different.
Lei Wang +5 more
doaj +1 more source
Nonlinear Projective Dictionary Pair Learning for PolSAR Image Classification
, 2021 Yanqiao Chen +5 more
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Remote SensingDeep learning methods have shown significant advantages in polarimetric synthetic aperture radar (PolSAR) image classification. However, their performances rely on a large number of labeled data.
Jianlong Wang +6 more
doaj +1 more source
Active Learning for PolSAR image classification
, 2013 One of the biggest problems, when supervised learning techniques are used, for training classifier, is the necessity of a big amount of labelled samples, including the problems and costs of carry out the labelling of the prototypes needed. SAR images are difficult to label due to the speckle noise, which increases the normal effort needed for labelling
openaire +2 more sources
Supervised PolSAR Image Classification with Multiple Features and Locally Linear Embedding [PDF]
, 2018 Qiang Zhang +3 more
openalex +1 more source