Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture

We provide sea ice classification maps of a subweekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar (SAR) and optical data in other MOSAiC studies

A robust nonlinear scale space change detection approach for SAR images

In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling (FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction and shape detail preservation. MSERs of each scale space image are found and then combined through a decision level fusion strategy, namely "selective scale fusion" (SSF), where contrast and boundary curvature of each MSER are considered. The performance of the proposed method is evaluated using real multitemporal high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change. One of the main outcomes of this approach is that different objects having different sizes and levels of contrast with their surroundings appear as stable regions at different scale space images thus the fusion of results from scale space images yields a good overall performance

Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees

A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set. Finally, a group of discrimination trees are learned and combined into a final classifier in the framework of Real-AdaBoost. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). Several comparative studies are conducted to evaluate the effectiveness of the proposed algorithm. Experimental results show the distinctive superiority of the proposed method under both standard operating conditions (SOCs) and extended operating conditions (EOCs). Moreover, our additional tests suggest that good recognition accuracy can be achieved even with limited number of training images as long as these are captured with appropriately incremental sample step in target poses

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

Optical and radar remotely sensed data for large-area wildlife habitat mapping

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models

Comparison of the Ice Watch Database and Sea Ice Classification from Sentinel-1 Imagery

In this thesis,we investigate the potential use of in-situ sea ice observations from the Ice Watch database as ground truth data for an automated classification algorithm of sea ice types from Sentinel-1 SAR data. The Ice Watch database and the Sentinel-1 data archive are searched for in-situ observations and satellite data acquisitions in Extra Wide swath mode overlapping in both space and time. Time differences of up to a maximum of 12 hours are accepted and included in this investigation. The Sentinel-1 data is downloaded in Ground- Range Detected format at medium resolution and thermal noise correction, radiometric calibration and additional multilooking with a 3-by-3 window is applied. Different ice types in the images are then classified with the Gaussian IA classifier developed at UiT. The resulting image with ice type labels is geolocated and aligned with the in-situ observation from the Ice Watch database. A grid of 25-by-25 pixels around the location of the Ice Watch observation is extracted. For data points with a large time difference between in-situ observation and satellite data acquisition, a sea ice drift algorithm is applied to estimate and correct for possible influence of ice drift between the two acquisition times. Correlation and linear regression is investigated between a total number of 123 observation and the classified area around the observation. In addition, per class accuracy for the trained ice types in the classifier is investigated. A medium to strong positive correlation is found between types of ice and a weakly negative to no correlation was found for sea ice concentration. “Second-/Multiyear ice” separation achieves the highest score with 93.8 % per class accuracy. The second highest scoring class is “Deformed First-Year Ice”, for which 48.1 % per class accuracy is achieved. The thinner ice performs poorly due to the low number of representative of observations from these classes. Based on the findings there is a relationship between the reported observations from the Ice Watch database and the classified Sentinel-1 images. The ability to separate the older and deformed ice types from younger level ice is present

Remote Sensing Image Analysis via a Texture Classification Neural Network

In this work we apply a texture classification network to remote sensing image analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain

Assessing the potential of sentinel-1 and sentinel-2 satellite imagery for shoreline classification in support of oil spill preparedness and response

Coastal zones are critical ecosystems that provide important habitat for marine animals, fish, shellfish, birds, and many other species. However, there is a risk of mineral oil impacting in these areas due to human activities offshore. Shoreline classification is the first step to establishing response contingency plans in case of an oil spill. This study estimates the potential of using open-access, high-resolution Sentinel-1 and Sentinel-2 imagery for the mapping of shoreline types in support of oil spill preparedness and response activities. The two classification maps, depicting shoreline and coastal land cover, were produced using an advanced object-based Random Forest (RF) algorithm. Various features extracted from multi-source data, including spectral, texture, ratio, polarimetric features, and digital elevation model (DEM), were analyzed to identify the most valuable features for discrimination between different shoreline types. Multiple classification scenarios with the most useful features were then assessed and compared to find the best classification model. The developed algorithm achieved accuracies of 87.10% and 84.75% of coastal land cover and shoreline maps. These results demonstrated the high potential of using freely available Sentinel-1 and -2 satellite data for coastal mapping

A Pattern Classification Based approach for Blur Classification

Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach