An Advanced Non-Gaussian Feature Space Method for POL-SAR Image Segmentation

This work extends upon our simple feature-based multichannel SAR segmentation method to incorporate highly desirable statistical properties into a computationally simple approach. The desirable properties include Markov random field contextual smoothing and goodness-of-fit testing to automatically obtain the significant number of classes. To achieve this we need to find an explicit class model to fit these non-Gaussian, non-symmetric or skewed feature space clusters. We take the skewed scale mixture of Gaussian scheme to model our classes and approximate it by a number of constrained Gaussians, thereby retaining much of the speed and simplicity of the original feature space method. The algorithm will be demonstrated on a real data and compared to an automatic Gaussian model

Learning to Estimate Sea Ice Concentration from SAR Imagery

Through the growing interest in the Arctic for shipping, mining and climate research, large-scale high quality ice concentration is of great interest. Due to the unavailability of suitable ice concentration estimation algorithms, ice concentration maps are interpreted from synthetic aperture radar (SAR) images manually by ice experts for operational uses. An automatic ice concentration estimation algorithm is required for accurate large-scale ice mapping. In this thesis, a set of algorithms are developed aiming at operational ice concentration estimation from SAR images. The major difficulty in designing a robust algorithm for ice concentration estimation from SAR images is the constantly changing SAR image features of ice and water in time and location. This difficulty is addressed by learning features instead of designing features from SAR images. A set of convolutional neural network based ice concentration estima- tion algorithms are developed to learn multi-scale SAR image features and simultaneously regress ice concentration from the learned image features. We first demonstrated the capa- bility of CNNs in ice concentration estimation from SAR images when trained using image analysis charts as ground truth. Then the model is further improved by taking into account the errors in the image analysis charts. Ice concentration estimates with improved robust- ness to training samples errors, accuracy and scale of details are obtained. The robustness of the developed methods are further demonstrated in the melt season of the Beaufort Sea, where reasonable ice concentration estimates are acquired. In order to reduce the model training time, it is desired to reuse existing models. The model transferability is evaluated and suggestions on using existing models to accelerate the training process are given, which is shown to reduce the training time by over 10 times in our case

Cryosphere Applications

Synthetic aperture radar (SAR) provides large coverage and high resolution, and it has been proven to be sensitive to both surface and near-surface features related to accumulation, ablation, and metamorphism of snow and firn. Exploiting this sensitivity, SAR polarimetry and polarimetric interferometry found application to land ice for instance for the estimation of wave extinction (which relates to sub surface ice volume structure) and for the estimation of snow water equivalent (which relates to snow density and depth). After presenting these applications, the Chapter proceeds by reviewing applications of SAR polarimetry to sea ice for the classification of different ice types, the estimation of thickness, and the characterisation of its surface. Finally, an application to the characterisation of permafrost regions is considered. For each application, the used (model-based) decomposition and polarimetric parameters are critically described, and real data results from relevant airborne campaigns and space borne acquisitions are reported

On the use of the l(2)-norm for texture analysis of polarimetric SAR data

In this paper, the use of the l2-norm, or Span, of the scattering vectors is suggested for texture analysis of polarimetric synthetic aperture radar (SAR) data, with the benefits that we need neither an analysis of the polarimetric channels separately nor a filtering of the data to analyze the statistics. Based on the product model, the distribution of the l2-norm is studied. Closed expressions of the probability density functions under the assumptions of several texture distributions are provided. To utilize the statistical properties of the l2-norm, quantities including normalized moments and log-cumulants are derived, along with corresponding estimators and estimation variances. Results on both simulated and real SAR data show that the use of statistics based on the l2-norm brings advantages in several aspects with respect to the normalized intensity moments and matrix variate log-cumulants.Peer ReviewedPostprint (published version

Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments

Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For this task, an unsupervised coastline detection procedure based on scale-space representations and K-medians clustering as well as morphological image post-processing is proposed. Since this procedure exploits a clear discriminability of "dark" and "bright" appearances of water and land surfaces, respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data acquired in pursuit monostatic mode is expected to provide a promising benefit. In addition, we investigate the benefit introduced by a utilization of a non-local InSAR filter for amplitude denoising and coherence estimation instead of a conventional box-car filter. Experiments carried out on real TanDEM-X pursuit monostatic data confirm our expectations and illustrate the advantage of the employed data configuration over conventional TanDEM-X products for automatic coastline detection

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure