Riemannian Nearest-Regularized Subspace Classification for Polarimetric SAR images

01/02/2022
by   Junfei Shi, et al.
0

As a representation learning method, nearest regularized subspace(NRS) algorithm is an effective tool to obtain both accuracy and speed for PolSAR image classification. However, existing NRS methods use the polarimetric feature vector but the PolSAR original covariance matrix(known as Hermitian positive definite(HPD)matrix) as the input. Without considering the matrix structure, existing NRS-based methods cannot learn correlation among channels. How to utilize the original covariance matrix to NRS method is a key problem. To address this limit, a Riemannian NRS method is proposed, which consider the HPD matrices endow in the Riemannian space. Firstly, to utilize the PolSAR original data, a Riemannian NRS method(RNRS) is proposed by constructing HPD dictionary and HPD distance metric. Secondly, a new Tikhonov regularization term is designed to reduce the differences within the same class. Finally, the optimal method is developed and the first-order derivation is inferred. During the experimental test, only T matrix is used in the proposed method, while multiple of features are utilized for compared methods. Experimental results demonstrate the proposed method can outperform the state-of-art algorithms even using less features.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 5

05/30/2018

Multiple Manifolds Metric Learning with Application to Image Set Classification

In image set classification, a considerable advance has been made by mod...
08/13/2020

Linear pooling of sample covariance matrices

We consider covariance matrix estimation in a setting, where there are m...
08/06/2019

Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning

The importance of wild video based image set recognition is becoming mon...
02/23/2022

Robust Geometric Metric Learning

This paper proposes new algorithms for the metric learning problem. We s...
11/09/2020

Coupled regularized sample covariance matrix estimator for multiple classes

The estimation of covariance matrices of multiple classes with limited t...
03/27/2019

Non-Iterative Subspace-Based DOA Estimation in the Presence of Nonuniform Noise

The uniform white noise assumption is one of the basic assumptions in mo...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.