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.
Highlights
As an advanced SAR system, polarimetric SAR (PolSAR) inherits the unique advantages of SAR, and it can transmit and receive electromagnetic waves in four polarization combinations (HH, VV, HV, and VH)
PolSAR data have been widely applied in image classification, target recognition and detection task, among which image classification plays an important role in remote sensing image interpretation systems
This paper proposes a PolSAR image classification algorithm based on a multi-parallel fully convolutional networks (FCNs) and a manifold graph embedding model
Summary
As an advanced SAR system, polarimetric SAR (PolSAR) inherits the unique advantages of SAR, and it can transmit and receive electromagnetic waves in four polarization combinations (HH, VV, HV, and VH). PolSAR data have been widely applied in image classification, target recognition and detection task, among which image classification plays an important role in remote sensing image interpretation systems. The main task of PolSAR image classification is to determine the true region category by mining the specific image information, that is, assigning a category label to each pixel included in the image. SAR images have a different imaging mechanism than optical images, and their readability and scene comprehensibility are very poor; on the other hand, compared with optical remote sensing images or single-polarized SAR images, PolSAR images provide richer ground information with far more complicated data formats, making automatic interpretation extremely difficult. Research on PolSAR image classification has always been a research hotspot in the field of remote sensing
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