Discriminant Manifold Learning with Graph Convolution Based Regression for Image Classification

2019 ◽  
pp. 226-236
Author(s):  
Ruifeng Zhu ◽  
Fadi Dornaika ◽  
Yassine Ruichek
Keyword(s):  
Image Classification ◽  
Manifold Learning

scholarly journals Nonlinear Manifold Learning Integrated with Fully Convolutional Networks for PolSAR Image Classification

2020 ◽  
Vol 12 (4) ◽  
pp. 655
Author(s):  
Chu He ◽  
Mingxia Tu ◽  
Dehui Xiong ◽  
Mingsheng Liao
Keyword(s):  
Image Classification ◽  
Manifold Learning ◽  
Feature Learning ◽  
Learning Ability ◽  
High Dimensional ◽  
Compact Representation ◽  
Deep Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Spatial Features

Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s application ability. Most existing classification methods for PolSAR imagery are based on manual features, such methods with fixed pattern has poor data adaptability and low feature utilization, if directly input to the classifier. Therefore, combining PolSAR data characteristics and deep network with auto-feature learning ability forms a new breakthrough direction. In fact, feature learning of deep network is to realize function approximation from data to label, through multi-layer accumulation, but finite layers limit the network’s mapping ability. According to manifold hypothesis, high-dimensional data exists in potential low-dimensional manifold and different types of data locates in different manifolds. Manifold learning can model core variables of the target, and separate different data’s manifold as much as possible, so as to complete data classification better. Therefore, taking manifold hypothesis as a starting point, nonlinear manifold learning integrated with fully convolutional networks for PolSAR image classification method is proposed in this paper. Firstly, high-dimensional polarized features are extracted based on scattering matrix and coherence matrix of original PolSAR data, whose compact representation is mined by manifold learning. Meanwhile, drawing on transfer learning, pre-trained Fully Convolutional Networks (FCN) model is utilized to learn deep spatial features of PolSAR imagery. Considering complementary advantages, weighted strategy is adopted to embed manifold representation into deep spatial features, which are input into support vector machine (SVM) classifier for final classification. A series of experiments on three PolSAR datasets have verified effectiveness and superiority of the proposed classification algorithm.

Hierarchical discriminant manifold learning for dimensionality reduction and image classification

2015 ◽  
Vol 24 (5) ◽  
pp. 053015 ◽  
Author(s):  
Weihai Chen ◽  
Changchen Zhao ◽  
Kai Ding ◽  
Xingming Wu ◽  
Peter C. Y. Chen
Keyword(s):  
Dimensionality Reduction ◽  
Image Classification ◽  
Manifold Learning

scholarly journals HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON MANIFOLD DATA ANALYSIS AND SPARSE SUBSPACE PROJECTION

2021 ◽  
Vol 8 (9) ◽  
pp. 36-45
Author(s):  
ZHENG Zhijun ◽  
PENG Yanbin
Keyword(s):  
Data Analysis ◽  
Image Classification ◽  
Manifold Learning ◽  
Hyperspectral Image ◽  
Optimization Method ◽  
Coefficient Matrix ◽  
New Method ◽  
Small Samples ◽  
Subspace Projection ◽  
Hyperspectral Image Classification

Aiming at the problem of "dimension disaster" in hyperspectral image classification, a method of dimension reduction based on manifold data analysis and sparse subspace projection (MDASSP) is proposed. The sparse coefficient matrix is established by the new method, and the sparse subspace projection is carried out by the optimization method. To keep the geometric structure of the manifold, the objective function is regularized by the manifold learning method. The new method combines sparse coding and manifold learning to generate features with better classification ability. The experimental results show that the new method is better than other methods in the case of small samples.

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