DEM Generation Combining SAR Polarimetry and Shape-From-Shading Techniques

In this paper a practical method is demonstrated for estimating terrain slopes in azimuth and ground range directions for digital elevation model (DEM) generation without any prior knowledge on the terrain by using only one single pass of polarimetric synthetic aperture radar (PolSAR) instead of two-pass or interferometric SAR (INSAR). The basic approach is by combing the orientation angle estimation and a shape-from-shading technique (SFS) which is mostly used by the computer vision community. In particular, when limited PolSAR data are available, this technique provides an alternative way for DEM generation. The polarization orientation angle (POA) is related to both the range and azimulh angles of the tilted surface and radar viewing angle and it can be estimated from the PolSAR data by using the circular polarization method which shows the best performance in computation efficiency and accuracy with respect to the other methods. After terrain slopes in both the range and azimuth directions have been estimated initially by the combination of the POA estimation and the SFS algorithm, a least squares method similar to that used in interferometric phase unwrapping is used to generate the topography. The least squares approach to phase unwrapping obtains an unwrapped solution by minimizing the differences between the discrete partial derivatives of the (wrapped) phase data and the discrete partial derivatives of the unwrapped solution. We illustrate our results by using the polarimetric SAR images acquired in Algeria by the RadarSAT2 (FQ19) in C-band.

Classification of the polarimetric synthetic aperture radar (PolSAR) data is an important facet of synthetic aperture radar (SAR) image interpretation. In general, many polarimetric features can be extracted from PolSAR data. However, not all features are benefit for classification and too many polarimetric features result in the low classification efficiency. We introduce a method to deal with many polarimetric features and obtain both high classification accuracy and high classification efficiency. We first preprocess the PolSAR data to obtain objects, i.e., homogeneous regions. Next, we apply polarimetric feature processing to compute various polarimetric features of the PolSAR data. Based on the classification of the sample information, we then select the prominent polarimetric features using the data mining attributes of decision tree algorithm (DTA). Finally, we obtain classification results through the selected polarimetric features by DTA, the sample data and support vector machine (SVM). We verified the proposed method using the AirSAR L-Band PolSAR data. Our experiments indicate that the classification accuracy of the proposed method is equivalent to that of SVM and the computational efficiency is comparable to that of DTA. Thus, the proposed method integrates the advantages of both DTA and SVM.

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for digital elevation model (DEM) generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the high-density characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented.

Polarimetric synthetic aperture radar (PolSAR) image interpretation is widely used in various fields. Recently, deep learning has made significant progress in PolSAR image classification. Supervised learning (SL) requires a large amount of labeled PolSAR data with high quality to achieve better performance, however, manually labeled data are insufficient. This causes the SL to fail into overfitting and degrades its generalization performance. Furthermore, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scattering confusion</i> problem is also a significant challenge that attracts more attention. To solve these problems, this article proposes a heterogeneous contrastive learning method (HCLNet). It aims to learn high-level representation from unlabeled PolSAR data for few-shot classification according to multifeatures and superpixels. Beyond the conventional CL, HCLNet introduces the heterogeneous architecture for the first time to utilize heterogeneous PolSAR features better. And it develops two easy-to-use plugins to narrow the domain gap between optics and PolSAR, including feature filter and superpixel-based instance discrimination, which the former is used to enhance the complementarity of multifeatures, and the latter is used to increase the diversity of negative samples. Experiments demonstrate the superiority of HCLNet on three widely used PolSAR benchmark datasets compared with state-of-the-art methods. Ablation studies also verify the importance of each component. Besides, this work has implications for how to efficiently utilize the multifeatures of PolSAR data to learn better high-level representation in CL and how to construct networks suitable for PolSAR data better.

Building orientation with respect to the radar look direction has a critical influence on the interpretation of multilook polarimetric synthetic aperture radar (PolSAR) data in urban areas. In this paper, its impacts on polarimetric orientation angle (POA) estimation and model-based decomposition are discussed. The discussion begins with the analysis of the general double-bounce scattering model, of which the characteristics are dependent on the electromagnetic and geometric parameters of the related dihedral structure. Then, for multilook PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, the impacts of the building orientation on POA estimation can be revealed. With the increase of the building orientation, the POA difference between the two dihedral structures increases gradually, and the feasibility to estimate the building orientation via the estimated POA is reduced dramatically. Upon further analysis, we illustrate the impacts on the model-based decomposition. With the increase of the building orientation, the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the processing of POA compensation, which is helpful in reducing the impacts of the building orientation, also becomes invalid when the building orientation increases to a certain value. At last, three L-band data sets of San Francisco acquired by AIRSAR are used to verify the inferences. The experimental results show that, for L-band PolSAR data in urban areas, when the radar look angle is around 45, the threshold of building orientation for the validity of dominant scattering mechanism labeling is about ±3, and for the POA compensation, the threshold is about ±12.

This paper presents an application of the recent advances in the field of spherically invariant random vector (SIRV) modeling for coherency matrix estimation in heterogeneous clutter. The complete description of the polarimetric synthetic aperture radar (POLSAR) data set is achieved by estimating the span and the normalized coherency independently. The normalized coherency describes the polarimetric diversity, while the span indicates the total received power. The main advantages of the proposed fixed-point (FP) estimator are that it does not require any <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information about the probability density function of the texture (or span) and that it can directly be applied on adaptive neighborhoods. Interesting results are obtained when coupling this FP estimator with an adaptive spatial support based on the scalar span information. Based on the SIRV model, a new maximum-likelihood distance measure is introduced for unsupervised POLSAR classification. The proposed method is tested with both simulated POLSAR data and airborne POLSAR images provided by the Radar Ae¿roporte¿ Multi-Spectral d'Etude des Signatures system. Results of entropy/alpha/anisotropy decomposition, followed by unsupervised classification, allow discussing the use of the normalized coherency and the span as two separate descriptors of POLSAR data sets.

This letter presents a new estimation scheme for optimally deriving clutter parameters with high-resolution polarimetric synthetic aperture radar (POLSAR) data. The heterogeneous clutter in POLSAR data is described by the spherically invariant random vector model. Three parameters are introduced for the high-resolution POLSAR data clutter: the span, the normalized texture, and the speckle normalized covariance matrix. The asymptotic distribution of the novel span estimator is investigated. A novel heterogeneity test for the POLSAR clutter is also discussed. The proposed method is tested with airborne POLSAR images provided by the Office National d'Études et de Recherches Aerospatiales Radar Aéroporté Multi-spectral d'Etude des Signatures system.

ABSTRACTThis paper proposes a new algorithm, for polarimetric synthetic aperture radar (PolSAR) classification, based on a stacked auto-encoder and scattering energy. Previous approaches to PolSAR classification predominantly consider only the single pixel of distribution of the polarimetric data and scattering characteristics, and ignore other kinds of image features like the relationship of the local pixels. Besides, because of the complexities of PolSAR data, it is difficult to compute the derivatives that are needed for back-propagation in deep-learning classifiers. To overcome these difficulties, we propose a new approach that combines the scattering power and stacks sparse auto-encoder (Scattering SSAE) for PolSAR classification. Firstly, orientation compensation is used to compensate the polarization orientation angle, reducing the impact of polarimetric angle noise. Secondly, Freeman-Durden decomposition is adopted to extract three basic scattering powers: surface, double bounce and volume. Each PolSAR image pixel is transformed into these scattering powers, yielding a new kind of feature from the PolSAR data. Finally, using the three kinds of scattering power as inputs, we combine local spatial information using a patch-based approach, and use a deep learning architecture to achieve classification. We compare our method against several other state-of-the-art methods using ground-truthed test-data, and show that the Scattering SSAE method achieves higher accuracy than other methods on most categories.

The Digital Elevation Models (DEMs) are an important source of topographical data for many scientific and engineering applications. Where topographical data are unavailable, global coverage elevation data sets, typically DEMs from remotely sensed data, are the main sources of such information. Interferometric SAR (InSAR), is a useful method for low-cost, relatively precise and wide-coverage surface DEM generation. However, ground deformation should somehow be excluded from InSAR-based DEM generation. To identify surface deformation areas, the so-called Differential InSAR (DInSAR) is a commonly used method. In this paper, the authors propose a two-step DEM generation method: the ground deformation area detection using DInSAR technique and deformation area exclusion in InSAR DEM generation by detected mask.

This article proposes a novel autoencoder (AE) network based on the distribution of polarimetric synthetic aperture radar (POL-SAR) data matrix, called a mixture autoencoder (MAE). Through a detailed analysis of the data distribution POL-SAR data matrix, a normalization method is also presented in succession. The proposed MAE defines the data error term in the loss function according to the data distribution. It can be regarded as a process of unsupervised feature extraction designed specifically for POL-SAR data matrix. Then, a softmax classifier is trained with the help of data features and the corresponding label information. Next, a stacked MAE (SMAE) network is reasonably constructed by considering the data distribution among different layers. Finally, this article also presents a classification network through discarding the decoder process of the proposed SMAE and connecting with a softmax classifier. The SMAE is trained layer by layer using the unlabeled data. The softmax classifier is also trained with a small number of labeled pixels. With parameters obtained from the above-mentioned procedures as the initial parameters, the whole classification network is trained by the labeled pixels to get a well-trained model, which is used for predicting the corresponding label of the pixel in the data set. Three real POL-SAR data sets, including the AIR-SAR L-band data of Flevoland, The Netherlands, are used in the experiments. Compared with one classical algorithm and two related models with the similar structure, both the proposed methods show improvements in overall accuracy and efficiency as well as possess better adaptability of the parameter and preferable consistency with the classification performance.

Multi-Layer Perceptrons (MLPS) are powerful function approximators. In the last decades they were successfully applied to many different regression and classification problems. Their characteristics and convergence properties are well studied and relatively well understood, but they were originally designed to work with real-valued data. The main focus of this paper is the classification of polarimetric synthetic aperture radar (POLSAR) data which are a complex-valued signal. Instead of using an arbitrarily projection of this complex-valued data to the real domain, the paper proposes the usage of complex-valued MLPS (CV-MLPS), which are an extension of MLPS to the complex domain. The paper provides a detailed yet general derivation of the complex backpropagation algorithm and mentions related problems as well as possible solutions. Furthermore, it evaluates the performance of CV-MLPS in a land-cover classification task in POLSAR images under several learning conditions, and compares the proposed classifier with standard methods. The experimental results show that CV-MLPS are successfully applicable to classification tasks in POLSAR data. They show good convergence properties and a better performance if compared to real-valued MLPS.

The classification of urban areas in polarimetric synthetic aperture radar (PolSAR) data is a challenging task. Moreover, urban structures oriented away from the radar line of sight pose an additional complexity in the classification process. The characterization of such areas is important for disaster relief and urban sprawl monitoring applications. In this paper, a novel technique based on deep learning is proposed, which leverages a synthetic target database for data augmentation. The PolSAR dataset is rotated by uniform steps and collated to form a reference database. A stacked autoencoder network is used to transform the information in the augmented dataset into a compact representation. This significantly improves the generalization capabilities of the network. Finally, the classification is performed by a multilayer perceptron network. The modular architecture allows for easy optimization of the hyperparameters. The synthetic target database is created and the classification performance is evaluated on an Lband airborne UAVSAR dataset and L-band space-borne ALOS-2 dataset acquired over San Francisco, USA. The proposed technique shows an overall accuracy of 91.3%. An improvement over state-of-the-art techniques is achieved, especially in urban areas rotated away from the radar line of sight.

Abstract. In recent years, the use of Polarimetric Synthetic Aperture Radar (PolSAR) data in different applications dramatically has been increased. In SAR imagery an interference phenomenon with random behavior exists which is called speckle noise. The interpretation of data encounters some troubles due to the presence of speckle which can be considered as a multiplicative noise affecting all coherent imaging systems. Indeed, speckle degrade radiometric resolution of PolSAR images, therefore it is needful to perform speckle filtering on the SAR data type. Markov Random Field (MRF) has proven to be a powerful method for drawing out eliciting contextual information from remotely sensed images. In the present paper, a probability density function (PDF), which is fitted well with the PolSAR data based on the goodness-of-fit test, is first obtained for the pixel-wise analysis. Then the contextual smoothing is achieved with the MRF method. This novel speckle reduction method combines an advanced statistical distribution with spatial contextual information for PolSAR data. These two parts of information are combined based on weighted summation of pixel-wise and contextual models. This approach not only preserves edge information in the images, but also improves signal-to-noise ratio of the results. The method maintains the mean value of original signal in the homogenous areas and preserves the edges of features in the heterogeneous regions. Experiments on real medium resolution ALOS data from Tehran, and also high resolution full polarimetric SAR data over the Oberpfaffenhofen test-site in Germany, demonstrate the effectiveness of the algorithm compared with well-known despeckling methods.

An unsupervised segmentation method of polarimetric synthetic aperture radar (POLSAR) data is proposed in this paper. Firstly, this method performs initial classification of the POLSAR data based on polarimetric target decomposition. Then a model for the conditional distribution of the polarimetric coherency matrix is combined with a Markov random field (MRF) representation for the distribution of the region label to obtain the posterior distribution. Finally, iterated conditional modes (ICM) method is used to obtain the final segmentation results. A new MRF model with variable parameter is used in this method. Experiment results indicate that the proposed method achieves improvement over standard MRF method. (4 pages)

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.