Synthetic Aperture Radar Image Time Research Articles

A phenological-knowledge-independent method for automatic paddy rice mapping with time series of polarimetric SAR images

Paddy rice, which sustains more than half of the global population, requires accurate and efficient mapping to ensure food security. Synthetic aperture radar (SAR) has become indispensable in this process due to its remarkable ability to operate effectively in adverse weather conditions and its sensitivity to paddy rice growth. Phenological-knowledge-based (PKB) methods have been commonly employed in conjunction with time series of SAR images for paddy rice mapping, primarily because they eliminate the need for training datasets. However, PKB methods possess inherent limitations, primarily stemming from their reliance on precise phenological information regarding paddy rice growth. This information varies across regions and paddy rice varieties, making it challenging to use PKB methods effectively on a large spatial scale, such as the national or global scale, where collecting comprehensive phenological data becomes impractical. Moreover, variations in farming practices and field conditions can lead to differences in paddy rice growth stages even within the same region. Using a generalized set of phenological knowledge in PKB methods may not be suitable for all paddy fields, potentially resulting in errors in paddy rice extraction. To address the challenges posed by PKB methods, this study proposed an innovative approach known as the phenological-knowledge-independent (PKI) method for mapping paddy rice using time series of Sentinel-1 SAR images. The central innovation of the PKI method lies in its capability to map paddy rice without relying on specific knowledge of paddy rice phenology or the need for a training dataset. This was made possible by the incorporation of three novel metrics: VH and VV normalized maximum temporal changes (NMTC) and VH temporal mean, derived from the distinctions between paddy rice and other land cover types in time series of SAR images. The PKI method was rigorously evaluated across three regions in China, each featuring different paddy rice varieties. Additionally, the PKI method was compared with two prevalent phenological-knowledge-based techniques: the automated paddy rice mapping method using SAR flooding signals (ARM-SARFS) and the manual interpretation of unsupervised clustering results (MI-UCR). The PKI method achieved an average overall accuracy of 97.99%, surpassing the ARM-SARFS, which recorded an accuracy of 89.65% due to errors stemming from phenological disparities among different paddy fields. Furthermore, the PKI method delivered results on par with the MI-UCR, which relied on the fusion of SAR and optical image time series, achieving an accuracy of 97.71%. As demonstrated by these findings, the PKI method proves highly effective in mapping paddy rice across diverse regions, all without the need for phenological knowledge or a training dataset. Consequently, it holds substantial promise for efficiently mapping paddy rice on a large spatial scale. The source code used in this study is available at https://code.earthengine.google.com/f82cf10cad64fa3f971ae99027001a6e.

Application of Gated Recurrent Unit Neural Network for Flood Extraction from Synthetic Aperture Radar Time Series

Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth’s surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Platforms such as Google Earth Engine (GEE) can provide a large amount of SAR data and preprocess it, providing powerful assistance for real-time flood monitoring and time series analysis. However, the application of long-term series data combined with recurrent neural networks (RNNs) to monitor floods has been lacking in current research, and the accuracy of flood extraction in open water surfaces remains unsatisfactory. In this study, we proposed a new method of near real-time flood monitoring with a higher accuracy. The method utilizes SAR image time series to establish a gated recurrent unit (GRU) neural network model. This model was used to predict normal flood-free surface conditions. Flood extraction was achieved by comparing and analyzing the actual flood surface conditions with the predicted conditions, using a parameter called Scores. Our method demonstrated significant improvements in accuracy compared to existing algorithms like the OTSU algorithm, Sentinel-1 Dual Polarized Water Index (SDWI) algorithm, and Z-score algorithm. The overall accuracy of our method was 99.20%, which outperformed the Copernicus Emergency Management Service (EMS) map. Importantly, our method exhibited high stability as it allowed for fluctuation within the normal range, enabling the extraction of the complete flood range, especially in open water surfaces. The stability of our method makes it suitable for the flood monitoring of future open-access SAR data, including data from future Sentinel-1 missions.

Bridging optical and SAR satellite image time series via contrastive feature extraction for crop classification

Precise crop mapping is crucial for guiding agricultural production, forecasting crop yield, and ensuring food security. Integrating optical and synthetic aperture radar (SAR) satellite image time series (SITS) for crop classification is an essential and challenging task in remote sensing. Previously published studies generally employ a dual-branch network to learn optical and SAR features independently, while ignoring the complementarity and correlation between the two modalities. In this article, we propose a novel method to learn optical and SAR features for crop classification through cross-modal contrastive learning. Specifically, we develop an updated dual-branch network with partial weight-sharing of the two branches to reduce model complexity. Furthermore, we enforce the network to map features of different modalities from the same class to nearby locations in a latent space, while samples from distinct classes are far apart, thereby learning discriminative and modality-invariant features. We conducted a comprehensive evaluation of the proposed method on a large-scale crop classification dataset. Experimental results show that our method consistently outperforms traditional supervised learning approaches, no matter the training samples are adequate or not. Our findings demonstrate that unifying the representations of optical and SAR image time series enables the network to learn more competitive features and suppress inference noise.

Multiscale framework for rapid change analysis from SAR image time series: Case study of flood monitoring in the central coast regions of Vietnam

Recently, the frequency of natural and environmental disasters has increased significantly, causing constant changes on the Earth's surface. Synthetic Aperture Radar (SAR) data have been proved to be useful for operational change monitoring tasks. The multiscale framework presented in this paper aims at detecting and analyzing changes using SAR image time series composed of large-size images. Spatio-temporal changes are initially detected at the subimage scale analysis stage to determine regions and image acquisition dates related to the change occurrence. Detailed changes are then identified at the pixel scale analysis stage between selected acquisitions at each recognized region. This framework was used for flood monitoring over a large area along the central coast of Vietnam (from Thua Thien Hue province to Quang Nam province). We exploited a Sentinel-1 image time series acquired during two rainy seasons and typhoon seasons in the Western Pacific (from September to December of the two years 2017 and 2018). The proposed framework detected flooded areas with a high overall accuracy of 90.4% and could analyze different types of changes that occurred in this time series, i.e. dirac, periodic, chaotic changes, and temporal stability.

K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series

In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix. As it is different from the state-of-the-art methods, which analyze the SAR image time series based on the change detection matrix (CDM), here, we directly use the distance matrix to determine changed pixels and extract change patterns. The proposed scheme involves two steps: change detection in SAR image time series and change-pattern discovery. First, these distance matrices are constructed for each spatial position over the time series by a dissimilarity measurement. The changed pixels are detected by using a thresholding algorithm on the energy feature map of all distance matrices. Then, according to the change detection results in SAR image time series, the changed areas for pattern mining are determined. Finally, the proposed K-Matrix algorithm which clusters distance matrices by the matrix cross-correlation similarity is used to group all changed pixels into different change patterns. Experimental results on two datasets of TerraSAR-X image time series illustrate the effectiveness of the proposed method.

Mapping of Bottomfast Lake Ice in the Northwest Territories Via Data Mining of Synthetic Aperture Radar Image Time Series

Changes in climate, warming temperatures and increased precipitation are impacting surface water resources in the Northwest Territories, Canada. Satellite remote sensing is an important tool to monitor variability in lake surface area, but monitoring depth is challenging. The distribution of bottomfast ice within a lake provides an indicator of depth and previous research shows that as lake ice develops and becomes bottomfast it exhibits a distinct signature when observed using multi-temporal Synthetic Aperture Radar (SAR) data. This research proposes an efficient computational technique for identifying bottom-fast ice across lakes in the Northwest Territories using multi-temporal SAR backscatter images and applies a function called dynamic time warping (DTW), which provides a shape-based similarity metric for time series data. We used backscatter profiles from surveyed lakes with known bottomfast ice to generate a DTW similarity metric on a pixel by pixel basis for a set of lakes. The similarity metric was used to categorize ice status as bottomfast or floating ice with 89.1% accuracy. DTW is an effective technique to map bottomfast ice using SAR time series and has potential to address limitations of other approaches where certain ice structures over deep lakes can produce backscatter responses similar to bottomfast ice.

Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach

Due to the constant increase in Earth Observation (EO) data collections, the monitoring of land cover is facilitated by the temporal diversity of the satellite images datasets. Due to the capacity of Synthetic Aperture Radar (SAR) sensors to operate independently of sunlight and weather conditions, SAR image time series offer the possibility to form a dataset with almost regular temporal sampling. This paper aims at mining the SAR image time series for an analysis of target’s behavior from the perspective of both temporal evolution and coherence. The authors present a two-level analytical approach envisaging the assessment of global (related to perceivable structures on the ground) and local (related to changes occurred within a perceivable structure on the ground) evolution inside the scene. The Latent Dirichlet Allocation (LDA) model is implemented to identify the categories of evolution present in the analyzed scene, while the statistical and coherent proprieties of the dataset’s images are exploited in order to identify the structures with stable electromagnetic response, the so-called Persistent Scatterers (PS). A comparative study of the two algorithms’ classification results is conducted on ERS and Sentinel-1 data. At global scale, the results fit human perception, as most of the points which can be exploited for Persistent Scatterers Interferometry (PS-InSAR) are classified within the same class, referring to stable structures. At local scale, the LDA classification demands for an extended number of classes (defined through a perplexity-based analysis), enabling further differentiation inside the evolutional character of those stable structures. The comparison against the map of detected PS reveals which classes present higher temporal correlation, determining a stable evolutionary character, opening new perspectives for validation of both PS detection and SITS analysis algorithms.

An Image Transform Based on Temporal Decomposition

Today, very dense synthetic aperture radar (SAR) time series are available through the framework of the European Copernicus Programme. These time series require innovative processing and preprocessing approaches including novel speckle suppression algorithms. Here we propose an image transform for hypertemporal SAR image time stacks. This proposed image transform relies on the temporal patterns only, and therefore fully preserves the spatial resolution. Specifically, we explore the potential of empirical mode decomposition (EMD), a data-driven approach to decompose the temporal signal into components of different frequencies. Based on the assumption that the high-frequency components are corresponding to speckle, these effects can be isolated and removed. We assessed the speckle filtering performance of the transform using hypertemporal Sentinel-1 data acquired over central Germany comprising 53 scenes. We investigated speckle suppression, ratio images, and edge preservation. For the latter, a novel approach was developed. Our findings suggest that EMD features speckle suppression capabilities similar to that of the Quegan filter while preserving the original image resolution.

Spatiotemporal Fuzzy Clustering Strategy for Urban Expansion Monitoring Based on Time Series of Pixel-Level Optical and SAR Images

Monitoring urban expansion dynamically using remote sensing technology is an essential method for obtaining and understanding urban spatial structure. However, the quality of traditional optical images in some areas is poor due to clouds and fog. Compared to optical images, synthetic aperture radar (SAR) can achieve earth observations without the limits of sunlight and weather conditions, but its speckle is too obvious. This paper combined the advantages of pixel-level optical image and SAR image time series and proposed a spatiotemporal fuzzy clustering (STFC) strategy for urban expansion monitoring. This strategy includes three parts: 1) the construction of optical-SAR image mixed time series; 2) a time-series fuzzy information granulation method to ascertain change nodes; and 3) STFC to determine the change types and range. In our study, 13 TM images and 25 SAR scenes taken from 2005 to 2011 were selected as raw data. We used the proposed method to monitor the urban expansion of Chengdu, China, and then, analyzed its main causes according to the monitoring results. The results suggested that: 1) the proposed methods could effectively extract the change nodes and change pixels, with the correctness of 85.20% and the completeness of 86.06%, outperforming the time series only (nonspatial) fuzzy clustering method, as well as traditional classification methods; and 2) the urban expansion of Chengdu is most apparent from 2005 to 2011, with the expansion direction shifting from the traditional ring structure expansion to point-axis expansion following the priority given to construction of new urban areas.

A Sparse Manifold Classification Method Based on a Multi-Dimensional Descriptive Primitive of Polarimetric SAR Image Time Series

Classification using the rich information provided by time-series and polarimetric Synthetic Aperture Radar (SAR) images has attracted much attention. The key point is to effectively reveal the correlation between different dimensions of information and form a joint feature. In this paper, a multi-dimensional SAR descriptive primitive for each single pixel is firstly constructed, which in the polarimetric scale obtains incoherent information through target decompositions while in the time scale obtains coherent information through stochastic walk. Secondly, for the purpose of feature extraction and dimension reduction, a special feature space mapping for the descriptive primitive of the whole image is proposed based on sparse manifold expression and compressed sensing. Finally, the above feature is inputted into a support vector machine (SVM) classifier. This proposed method can inherently integrate the features of polarimetric SAR times series. Experiment results on three real time-series polarimetric SAR data sets show the effectiveness of our presented approach. The idea of a multi-dimensional descriptive primitive as a convenient tool also opens a new spectrum of potential for further processing of polarimetric SAR image time series.

A framework of spatiotemporal fuzzy clustering for land-cover change detection using SAR time series

ABSTRACTCompared with optical satellite images, synthetic aperture radar (SAR) images are less influenced by weather conditions such as cloud and haze. With the support of SAR image time series, a framework of change detection based on spatiotemporal fuzzy clustering is presented. This framework mainly consists of three components: (1) pixel-level SAR image time-series modelling, based on scale invariant feature transform (SIFT); (2) probability analysis of change node based on iterative binary partition-mean square error model of the series is calculated to ascertain change nodes; (3) spatiotemporal fuzzy clustering is used to determine the types of change detection. To validate the method, 26 SAR images of the study area between 2004 and 2010 are utilized to monitor annual changes of cultivated land to construction land, and comparative experiments are conducted to evaluate the detection accuracy. Experimental results showed that the proposed framework could effectively extract the change nodes and change pixels, with correctness of 84.52% and completeness of 82.64%, outperforming the traditional fuzzy clustering method, as well as traditional classification methods.

Wavelet Operators and Multiplicative Observation Models—Application to SAR Image Time-Series Analysis

This paper first provides statistical properties of wavelet operators when the observation model can be seen as the product of a deterministic piecewise regular function (signal) and a stationary random field (noise). This multiplicative observation model is analyzed in two standard frameworks by considering either: 1) a direct wavelet transform of the model; or 2) a log-transform of the model prior to wavelet decomposition. The paper shows that, in Framework 1, wavelet coefficients of the time series are affected by intricate correlation structures which blur signal singularities. Framework 2 is shown to be associated with a multiplicative (or geometric) wavelet transform, and the multiplicative interactions between wavelets and the model highlight both sparsity of signal changes near singularities (dominant coefficients) and decorrelation of speckle wavelet coefficients. This paper then derives that, for time series of synthetic aperture radar data, geometric wavelets represent a more intuitive and relevant framework for the analysis of smooth earth fields observed in the presence of speckle. From this analysis, this paper proposes a fast-and-concise geometric-wavelet-based method for joint change detection and regularization of synthetic aperture radar image time series. In this method, geometric wavelet details are first computed with respect to the temporal axis in order to derive generalized-ratio change images from the time series. The changes are then enhanced, and speckle is attenuated by using spatial block sigmoid shrinkage. Finally, a regularized time series is reconstructed from the sigmoid shrunken change images. Some applications highlight relevancy of the method for the analysis of SENTINEL-1A and TerraSAR-X image time series over Chamonix Mont Blanc.

A First Assessment of the P-SBAS DInSAR Algorithm Performances Within a Cloud Computing Environment

We present in this work a first performance assessment of the Parallel Small BAseline Subset (P-SBAS) algorithm, for the generation of Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) deformation maps and time series, which has been migrated to a Cloud Computing (CC) environment. In particular, we investigate the scalable performances of the P-SBAS algorithm by processing a selected ENVISAT ASAR image time series, which we use as a benchmark, and by exploiting the Amazon Web Services (AWS) CC platform. The presented analysis shows a very good match between the theoretical and experimental P-SBAS performances achieved within the CC environment. Moreover, the obtained results demonstrate that the implemented P-SBAS Cloud migration is able to process ENVISAT SAR image time series in short times (less than 7 h) and at low costs (about USD 200). The P-SBAS Cloud scalable performances are also compared to those achieved by exploiting an in-house High Performance Computing (HPC) cluster, showing that nearly no overhead is introduced by the presented Cloud solution. As a further outcome, the performed analysis allows us to identify the major bottlenecks that can hamper the P-SBAS performances within a CC environment, in the perspective of processing very huge SAR data flows such as those coming from the existing COSMO-SkyMed or the upcoming SENTINEL-1 constellation. This work represents a relevant step toward the challenging Earth Observation scenario focused on the joint exploitation of advanced DInSAR techniques and CC environments for the massive processing of Big SAR Data.

Framework for monitoring the conversion of cultivated land to construction land using SAR image time series

The land cover change from cultivated land to construction land is a world issue since the urbanization process is extensively studied around the world. Chengdu, China, is a representative urbanization area, where cloud cover is very high most of the time, restricting the use of visible and near-infrared satellite data. Here, we present a novel framework for land change monitoring based on synthetic aperture radar (SAR) time series, which comprises three key components: (1) construction of pixel-level SAR image time series; (2) spatio-temporal similarity analysis based on morphological-structural characteristics of time series; and (3) iterative binary partition mean square error model analysis to ascertain change nodes. Experimental results showed that the proposed framework could effectively extract the change nodes and change pixels, with correctness of 85.82% and completeness of 84.78%, outperforming the time-series-only (non-spatial) method, as well as traditional classification methods, and the same framework using shorter Landsat TM image time series.

Technical Framework of Feature Extraction Based on Pixel-Level SAR Image Time Series

This study proposes a novel technical framework of feature extraction based on pixel-level synthetic aperture radar (SAR) image time series, to exploit the application potential of SAR image data with low and medium spatial resolution. This framework comprises three key parts: 1) construction of the pixel-level SAR image time series using a new matching technique based on progressive binary partition; 2) pixel-level similarity measurement via dynamic time warping (DTW); and 3) a new spatiotemporal similarity analysis method that improves feature extraction by considering both the similarity of a feature’s pixel-level time series and its spatial correlation. Two locations, covered by 31 low-resolution (150 m) and 26 medium-resolution (30 m) ENVISAT ASAR images, respectively, were selected as test cases to validate the proposed framework. Results show that the framework can identify features with a high level of accuracy, completeness, and correctness, outperforming methods using multitemporal images, as well as the time series-only (nonspatial) method, and other methods of spatiotemporal similarity analysis that use alternative similarity measures.

Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series

This paper presents a method for analyzing Synthetic Aperture Radar (SAR) and polarimetric SAR (PolSAR) image time series based on change detection matrices (CDM) containing information on changed and unchanged pixels. These matrices are constructed for each spatial position over the time series by implementing similarity cross tests. The proposed matrix is then exploited for adaptive temporal filtering, analysis of change dynamics and multitemporal change detection. The proposed approach is illustrated on the three following data sets: 25 ascending TerraSAR-X images and 7 descending RADARSAT 2 full polarization images over Chamonix-MontBlanc, France, where the seasonal evolution of glaciers and mountains can be observed, and a time series of 11 ascending ALOS-PALSAR dual polarization images over Merapi volcano, Indonesia during a period including the 2010 eruption.

Generation of Pixel-Level SAR Image Time Series Using a Locally Adaptive Matching Technique

Synthetic Aperture Radar (SAR) image time series play an important role in many applications. To construct pixel-level SAR image time series, we propose a locally adaptive image matching technique for the high-precision geometric registration of SAR images. The basic idea is to adapt the local characteristics of ground objects during the process of image registration. Then, by analyzing the spatial distribution of the error of each matched pair in the previous iteration, local areas are divided based on the local clustering of pairs with large errors. A new polynomial is then used to satisfy the local geometric constraint. Based on this proposed matching technique, we introduce a pixel-level SAR image time series modeling method. The experimental results show that the average geometric error of corresponding pixels in this algorithm is 0.073 pixels, while that of the NEST software is 0.242 pixels. The Pearson correlation coefficients of 20 pixels’ time series are above 0.85, indicating that the series bears high curve similarity and geometric precision, which suggests the proposed technique can provide high-quality SAR image time series.

Analysis of L- and C-Band SAR Image Time Series Over a Sahelian Area

This letter presents an analysis of two synthetic aperture radar (SAR) images time series over a Sahelian area, which is located in the Gourma region, Mali. The first one is acquired at the C-band by the Environment Satellite-Advanced SAR sensor in wide swath (WS) mode and the second one at the L-band by the ALOS-Phased Array-type L-band SAR in fine, dual, and WS modes. A change detection method based on random projection and the hue-saturation-value transform appears to be very well appropriated to detect the temporal changes appearing on both data sets. The results illustrate the higher penetration depth of the L-band over such sandy soils. The C-band time series is found to be very sensitive to surface changes (soil moisture and vegetation growth). By contrast, the L-band time series appear to be very stable over the sandy soils, i.e., the only temporal changes occurring over temporary or permanent ponds.

Multidate Divergence Matrices for the Analysis of SAR Image Time Series

The paper provides a spatio-temporal change detection framework for the analysis of image time series. In this framework, the detection of changes in time is addressed at the image level by using a matrix of cross-dissimilarities computed upon wavelet and curvelet image features. This makes possible identifying the acquisitions of interest: the acquisitions that exhibit singular behavior with respect to their neighborhood in the time series, and those that are representatives of some stationary behavior. These acquisitions of interest are compared at the pixel level to detect spatial changes characterizing the evolution of the time series. Experiments carried out over European Remote Sensing (ERS) and TerraSAR-X time series highlight the relevancy of the approach for analyzing synthetic aperture radar image time series.