Fully Convolutional Networks and a Manifold Graph Embedding-Based Algorithm for PolSAR Image Classification

Abstract

With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR image classification, feature extraction technology based on target decomposition is relatively mature, and how to extract discriminative spatial features and integrate these features with polarized information to maximize the classification accuracy is the core issue. In this context, this paper proposes a PolSAR image classification algorithm based on fully convolutional networks (FCNs) and a manifold graph embedding model. First, to describe different types of land objects more comprehensively, various polarized features of PolSAR images are extracted through seven kinds of traditional decomposition methods. Afterwards, drawing on transfer learning, the decomposed features are fed into multiple parallel and pre-trained FCN-8s models to learn deep multi-scale spatial features. Feature maps from the last layer of each FCN model are concatenated to obtain spatial polarization features with high dimensions. Then, a manifold graph embedding model is adopted to seek an effective and compact representation for spatially polarized features in a manifold subspace, simultaneously removing redundant information. Finally, a support vector machine (SVM) is selected as the classifier for pixel-level classification in a manifold subspace. Extensive experiments on three PolSAR datasets demonstrate that the proposed algorithm achieves a superior classification performance.

Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

“…FCN was developed on the basis of a classical CNN and was initially designed for pixel-wise image semantic segmentation [26,32,33]. Semantic segmentation, known as A brief overview of the proposed framework.…”

Evaluating Uniform Manifold Approximation and Projection for Dimension Reduction and Visualization of Polinsar Features

“…In (Ainsworth and Lee, 2004), the improvement of classification accuracy by preprocessing using ISOMAP is evaluated. More recently, manifold learning algorithms such as t-SNE and Locality Preserving Projections (LPP) are integrated into deep learning processes to combine principal features that reflect local back scattering properties with deep spatial features that capture a larger semantic context (He et al, 2020a, He et al, 2020b.…”

PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net

“…Since the scattering characteristics of the distributed targets for PolSAR images can be described by their coherency or covariance matrix [22], it is reasonable to make classification algorithms work directly on these complex-valued (CV) matrices. At the same time, this can also ease the aforementioned problems caused by the explicit feature extraction from original PolSAR CV data.…”

“…For the multi-feature extraction, first, the coherency matrix is directly adopted to represent the polarimetric second-order matrix feature (PSMF). This feature can retain the full polarization scattering information of PolSAR targets [22]. In addition, to suppress the interference of speckles and obtain smooth classification performance, the local mean feature (LMF) within coarse-scale superpixels is designed to obtain the spatial stationary information.…”