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The expensive acquisition of labeled data limits the practical use of supervised learning on polarimetric synthetic aperture radar (PolSAR) image analysis. Semi-supervised learning has attracted considerable attention as it can utilize few labeled data and very many unlabeled data. The scattering response of PolSAR data is strongly spatial distribution dependent, which provides rich information about land-cover properties. In this paper, we propose a semi-supervised learning method named multi-domain fusion graph network (MDFGN) to explore the multi-domain fused features including spatial domain and feature domain. Three major factors strengthen the proposed method for PolSAR image analysis. Firstly, we propose a novel sample selection criterion to select reliable unlabeled data for training set expansion. Multi-domain fusion graph is proposed to improve the feature diversity by extending the sample selection from the feature domain to the spatial-feature fusion domain. In this way, the selecting accuracy is improved. By few labeled data, very many accurate unlabeled data are obtained. Secondly, multi-model triplet encoder is proposed to achieve superior feature extraction. Equipped with triplet loss, limited training samples are fully utilized. For expanding training samples with different patch sizes, multiple models are obtained for the fused classification result acquisition. Thirdly, multi-level fusion strategy is proposed to apply different image patch sizes for different expanded training data and obtain the fused classification result. The experiments are conducted on Radarsat-2 and AIRSAR images. With few labeled samples (about 0.003–0.007%), the overall accuracy of the proposed method ranges between 94.78% and 99.24%, which demonstrates the proposed method's robustness and excellence. [ABSTRACT FROM AUTHOR]

The scattering response of polarimetric synthetic aperture radar (PolSAR) data is strongly target orientation-dependent. Formulating the polarimetric matrix as sequential data by rotating the polarimetric matrix along the radar line of sight would provide rich information about land-cover properties. In this work, we propose a composite sequential network (CSN) with polarization orientation angle (POA) attention to model the polarimetric coherency matrix sequence and explore target scattering orientation diversity features. Three major factors strengthen the proposed method for PolSAR image analysis. First, CSN improves the feature comprehensiveness by extending the interpretation mode of PolSAR data from spatial polarization to spatial polarization orientation. In this way, CSN could describe polarimetric response dynamics at different orientations. Second, a two-stream composite network with both real- and complex-valued convolutional long short-term memory (ConvLSTM) network is proposed to process the diagonal and off-diagonal elements of the coherency matrix sequence, respectively. Compared to existing real-/complex-valued networks, the CSN explores the significant phase information of the off-diagonal elements by operations in the complex domain. Meanwhile, CSN prevents padding 0 meaninglessly in the imaginary part of the real-valued diagonal elements. Third, during the sequential modeling of the polarimetric matrix, a POA attention mechanism is proposed. Equipped with POA-sensitive decomposition loss, the CSN attends to substantial POA range derived by targets’ physical scattering mechanism and learns features closely related to the scattering mechanism. Extensive experiments and analysis on land-cover classification demonstrate the proposed method’s robustness and excellence. [ABSTRACT FROM AUTHOR]

Landslides endanger regular industrial production and human safety. Displacement trend analysis gives us an explicit way to observe and forecast landslides. Although satellite-borne remote sensing methods such as synthetic aperture radar have gradually replaced manual measurement in detecting deformation trends, they fail to observe displacement in a north-south direction. Wireless low-cost GPS sensors have been developed to assist remote sensing methods in north-south deformation monitoring because of their high temporal resolution and wide usage. In our paper, a DLM-LSTM framework is developed to extract and predict north-south land deformation trends from meter accuracy GPS receivers. A dynamic linear model is introduced to model the relation between measurement and the state vector, including the trend, periodic variation, and autoregressive factors in a discontinuous low-cost latitude time series. The deformation trend with submeter-level accuracy is extracted by a Kalman filter and smoother. With validated input as in previous work, the power of an LSTM network is also shown in its ability to predict deformation trends in submeter-level accuracy. A submeter-level deformation trend is detected from wireless low-cost GPS sensors with meter-level navigation error. The framework will have broad application prospects in geological disaster monitoring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]