Land Cover Classification for Polarimetric SAR Images Based on Mixture Models

Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification

Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data. However, obtaining large number of accurately labeled samples of PolSAR data is very hard, which limits the practical use of deep learning. Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning. Inspired by the superior performance of deep mutual information in natural image feature learning and clustering, an end-to-end Convolutional Long Short Term Memory (ConvLSTM) network is used in order to learn the deep mutual information of polarimetric coherent matrices in the rotation domain with different polarimetric orientation angles (POAs) for unsupervised PolSAR image classification. First, for each pixel, paired “POA-spatio” samples are generated from the polarimetric coherent matrices with different POAs. Second, a special designed ConvLSTM network, along with deep mutual information losses, is used in order to learn the discriminative deep mutual information feature representation of the paired data. Finally, the classification results can be output directly from the trained network model. The proposed method is trained in an end-to-end manner and does not have cumbersome pipelines. Experiments on four real PolSAR datasets show that the performance of proposed method surpasses some state-of-the-art deep learning unsupervised classification methods.

PolSAR Image Classification Based on Statistical Distribution and MRF

Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes. In this study, we aim to find a robust and efficient method for polarimetric SAR (PolSAR) image classification. The Markov random field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this paper, we firstly introduce two ways to construct the Wishart mixture model and compare their performances using real PolSAR data. Then, the better mixture model and two other classical statistically distributions are combined with MRF to construct the MRF models. In order to improve the robustness of the models, the constant false alarm rate (CFAR)-based edge penalty term and an adaptive neighborhood system are embedded into the MRF energy functional. Classification is implemented in two schemes, i.e., pixel-based and region-based classifications. Finally, agriculture fields are used as the test scenario to evaluate the robustness and applicability of these algorithms.

A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin

With the increasing of satellite sensors, more available multi-source data can be used for large-scale high-precision crop classification. Both polarimetric synthetic aperture radar (PolSAR) and multi-spectral optical data have been widely used for classification. However, it is difficult to combine the covariance matrix of PolSAR data with the spectral bands of optical data. Using Hoekman's method, this study solves the above problems by transforming the covariance matrix to an intensity vector that includes multiple intensity values on different polarization basis. In order to reduce the features redundancy, the principal component analysis (PCA) algorithm is adopted to select some useful polarimetric and optical features. In this study, the PolSAR data acquired by satellite Gaofen-3 (GF-3) on 19 July 2017 and the optical data acquired by Sentinel-2A on 17 July 2017 over the Dongting lake basin are selected for the validation experiment. The results show that the full feature integration method proposed in this study achieves an overall classification accuracy of 85.27%, higher than that of the single dataset method or some other feature integration modes.

A Generalized Zero-Shot Learning Framework for PolSAR Land Cover Classification

Most supervised classification methods for polarimetric synthetic aperture radar (PolSAR) data rely on abundant labeled samples, and cannot tackle the problem that categorizes or infers unseen land cover classes without training samples. Aiming to categorize instances from both seen and unseen classes simultaneously, a generalized zero-shot learning (GZSL)-based PolSAR land cover classification framework is proposed. The semantic attributes are first collected to describe characteristics of typical land cover types in PolSAR images, and semantic relevance between attributes is established to relate unseen and seen classes. Via latent embedding, the projection between mid-level polarimetric features and semantic attributes for each land cover class can be obtained during the training stage. The GZSL model for PolSAR data is constructed by mid-level polarimetric features, the projection relationship, and the semantic relevance. Finally, the labels of the test instances can be predicted, even for some unseen classes. Experiments on three real RadarSAT-2 PolSAR datasets show that the proposed framework can classify both seen and unseen land cover classes with limited kinds of training classes, which reduces the requirement for labeled samples. The classification accuracy of the unseen land cover class reaches about 73% if semantic relevance exists during the training stage.

Gaofen-3 PolSAR Image Classification via XGBoost and Polarimetric Spatial Information

The launch of the Chinese Gaofen-3 (GF-3) satellite will provide enough synthetic aperture radar (SAR) images with different imaging modes for land cover classification and other potential usages in the next few years. This paper aims to propose an efficient and practical classification framework for a GF-3 polarimetric SAR (PolSAR) image. The proposed classification framework consists of four simple parts including polarimetric feature extraction and stacking, the initial classification via XGBoost, superpixels generation by statistical region merging (SRM) based on Pauli RGB image, and a post-processing step to determine the label of a superpixel by modified majority voting. Fast initial classification via XGBoost and the incorporation of spatial information via a post-processing step through superpixel-based modified majority voting would potentially make the method efficient in practical use. Preliminary experimental results on real GF-3 PolSAR images and the AIRSAR Flevoland data set validate the efficacy and efficiency of the proposed classification framework. The results demonstrate that the quality of GF-3 PolSAR data is adequate enough for classification purpose. The results also show that the incorporation of spatial information is important for overall performance improvement.

Classification of Multi-Frequency Polarimetric SAR Images Based on Multi-Linear Subspace Learning of Tensor Objects

One key problem for the classification of multi-frequency polarimetric SAR images is to extract target features simultaneously in the aspects of frequency, polarization and spatial texture. This paper proposes a new classification method for multi-frequency polarimetric SAR data based on tensor representation and multi-linear subspace learning (MLS). Firstly, each cell of the SAR images is represented by a third-order tensor in the frequency, polarization and spatial domains, with each order of tensor corresponding to one domain. Then, two main MLS methods, i.e., multi-linear principal component analysis (MPCA) and multi-linear extension of linear discriminant analysis (MLDA), are used to learn the third-order tensors. MPCA is used to analyze the principal component of the tensors. MLDA is applied to improve the discrimination between different land covers. Finally, the lower dimension subtensor features extracted by the MPCA and MLDA algorithms are classified with a neural network (NN) classifier. The classification scheme is accessed using multi-band polarimetric SAR images (C-, L- and P-band) acquired by the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) over the Flevoland area. Experimental results demonstrate that the proposed method has good classification performance in comparison with the classic multi-band Wishart classifier. The overall classification accuracy is close to 99%, even when the number of training samples is small.