ICE WATER CLASSIFICATION USING STATISTICAL DISTRIBUTION BASED CONDITIONAL RANDOM FIELDS IN RADARSAT-2 DUAL POLARIZATION IMAGERY

In this paper, Statistical Distribution based Conditional Random Fields (STA-CRF) algorithm is exploited for improving marginal ice-water classification. Pixel level ice concentration is presented as the comparison of methods based on CRF. Furthermore, in order to explore the effective statistical distribution model to be integrated into STA-CRF, five statistical distribution models are investigated. The STA-CRF methods are tested on 2 scenes around Prydz Bay and Adélie Depression, where contain a variety of ice types during melt season. Experimental results indicate that the proposed method can resolve sea ice edge well in Marginal Ice Zone (MIZ) and show a robust distinction of ice and water

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

Deep Semisupervised Teacher-Student Model Based on Label Propagation for Sea Ice Classification

In this article, we propose a novelteacher–student-based label propagation deep semisupervised learning (TSLP-SSL) method for sea ice classification based on Sentinel-1 synthetic aperture radar data. For sea ice classification, labeling the data precisely is very time consuming and requires expert knowledge. Our method efficiently learns sea ice characteristics from a limited number of labeled samples and a relatively large number of unlabeled samples. Therefore, our method addresses the key challenge of using a limited number of precisely labeled samples to achieve generalization capability by discovering the underlying sea ice characteristics also from unlabeled data. We perform experimental analysis considering a standard dataset consisting of properly labeled sea ice data spanning over different time slots of the year. Both qualitative and quantitative results obtained on this dataset show that our proposed TSLP-SSL method outperforms deep supervised and semisupervised reference methods

Toward Automated Ice-Water Classification on Large Northern Lakes Using RADARSAT-2 Synthetic Aperture Radar Imagery

Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability. These changes are expected to have implications for both human and environmental systems. Additionally, monitoring lake ice cover is required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently the Canadian Ice Service (CIS) monitors lakes using RADARSAT-2 SAR (synthetic aperture radar) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, the Iterative Region Growing using Semantics (IRGS) approach has been employed to perform ice-water classification on 61 RADARSAT-2 scenes of Great Bear Lake and Great Slave Lake over a three year period. This approach first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. These classes are manually labelled by the user, however automated labelling capability is currently in development. An accuracy assessment has been performed on the classification results, comparing outcomes with user-generated reference data as well as the CIS fraction reported at the time of image acquisition. The overall average accuracy of the IRGS method for this dataset is 92%, demonstrating the potential of this semi-automated method to provide detailed and reliable lake ice cover information

Sea Ice Mapping in Labrador Coast with Sentinel-1 Synthetic Aperture Radar Imagery

Sea ice mapping is crucial to Canadian coast, including marine transportation, environmental protection, resource management, disaster and emergency management, especially under current background of climate change. Canadian RADARSAT-2, like other synthetic aperture radar (SAR) sensors, is an essential source for current sea ice mapping in Canada, However, its limited revisiting makes daily ice chart generation challenging. The RADARSAT Constellation project is expected to be launched in 2018, the gap of data availability is expected to be filled with imagery from multiple sources. Sentinel-1, launched by European Space Agency (ESA) in late 2014, is an alternative source for sea ice mapping with comparable capability of RADARSAT-2 in wide swath mode. The main objective of this study is to examine the performance of Sentinel-1 imagery in sea ice mapping with a semi-automated image segmentation workflow. The methodology consists of two main steps. First, the most significant features in sea ice interpretation were determined using a random forest feature selection method. Second, an unsupervised graph-cut image segmentation is performed. The workflow was tested on 15 dual-polarized Sentinel-1A Extra Wide (EW) scenes in Labrador coast from December, 2015 to June, 2016, and the results were evaluated on the accuracy of water segmentation. The study found that: 1) GLCM features are effective in distinguishing different ice classes and 6 most important features were selected; 2) the proposed semi-automated workflow is able to segment Sentinel-1 imagery into 3 to 8 classes for water identification; and 3) generally Sentinel-1 imagery has similar responses from first-year ice compared with previous sensors, but with a different noise pattern in cross-polarized bands; and the overall accuracy of water identification reached close to 95%

Automated Remote Sensing Image Interpretation with Limited Labeled Training Data

Automated remote sensing image interpretation has been investigated for more than a decade. In early years, most work was based on the assumption that there are sufficient labeled samples to be used for training. However, ground-truth collection is a very tedious and time-consuming task and sometimes very expensive, especially in the field of remote sensing that usually relies on field surveys to collect ground truth. In recent years, as the development of advanced machine learning techniques, remote sensing image interpretation with limited ground-truth has caught the attention of researchers in the fields of both remote sensing and computer science. Three approaches that focus on different aspects of the interpretation process, i.e., feature extraction, classification, and segmentation, are proposed to deal with the limited ground truth problem. First, feature extraction techniques, which usually serve as a pre-processing step for remote sensing image classification are explored. Instead of only focusing on feature extraction, a joint feature extraction and classification framework is proposed based on ensemble local manifold learning. Second, classifiers in the case of limited labeled training data are investigated, and an enhanced ensemble learning method that outperforms state-of-the-art classification methods is proposed. Third, image segmentation techniques are investigated, with the aid of unlabeled samples and spatial information. A semi-supervised self-training method is proposed, which is capable of expanding the number of training samples by its own and hence improving classification performance iteratively. Experiments show that the proposed approaches outperform state-of-the-art techniques in terms of classification accuracy on benchmark remote sensing datasets.4 month