Antarctic Sea-Ice Classification Based on Conditional Random Fields From RADARSAT-2 Dual-Polarization Satellite Images

Abstract

In January 2014, Chinese National Antarctic Research Expedition (CHINARE) 30th cruise raised public concern since the Xuelong, the Chinese polar research vessel, was trapped in the sea-ice zone (66°39 20.88 S, 144°25 2.28 E) in the vicinity of the Adélie Depression area on the east Antarctic continent. This event highlighted the importance of an operational sea-ice classification map for ice routing to serve ship navigation. In this paper, unprecedented Antarctic sea-ice classification algorithms from RADARSAT-2 satellite dual-polarization synthetic aperture radar (SAR) images were developed using the conditional random fields (CRF) approach by including multiple features from sea-ice concentration, gray-level cooccurrence matrix textures, polarization ratio, backscatter coefficients, and intensity data. Coincident RADARSAT-2 Satellite SAR datasets with five scenes were collected for ice classification into categories such as open water, thin ice, smooth first year ice, deformed first year ice, and old ice during the CHINARE-30th cruise. The effects of deformation, rafting, and ridging during the spring-summer transition period were overwhelmed by the spatial and contextual CRF models in combination with the rich features extracted for sea-ice classification. Four strategies including statistical distribution and region connection, multiple features and support vector machine (SVM) integrated into the CRF model are proposed to describe the sea-ice-type relationships among pixels. By conducting comparative experiments between the proposed methods and state-of-the-art sea-ice classification based on the SVM algorithm, the best was obtained from the SVM-based CRF (SVM-CRF) algorithm for sea-ice classification with respect to the three scenes from the Indian Ocean sector and two scenes of Pacific Ocean sectors including medium-resolution dual-polarization SAR imagery with a pixel spacing of 50 m and higher resolution dual-polarization SAR imagery with a pixel spacing of 6.25 m. Results indicate that the SVM-CRF approach has the capacity for improving sea-ice classification, which can provide accurate and reliable sea-ice class information for sea-ice analysis.

Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method

“…Haralick and Shanmugam [31] first introduced the GLCM, which described the frequency of one gray level appearing in a specified spatial linear relationship with another gray level within the area under investigation and originally proposed 14 GLCM statistical parameters to label the GLCM. From then on, these GLCM statistical parameters have been widely used in image recognition, such as detecting sea ice changing in images [32][33][34], classifying landscape images [35], and distinguishing computed tomography (CT) images of normal and abnormal tissues [36,37]. However, it is difficult to understand the essential meanings of GLCM statistical parameters and to visibly check how these GLCM parameters can affect detection results since GLCM statistical parameters represent abstract characteristics of an object.…”

Advance in Sea Ice Studies in India

“…Zhu et al (2016) have used Radarsat-2 dual-polarization satellite images to develop an algorithm to classify Antarctic sea ice based on conditional Random Fields (CRF) approach by including multiple features from sea-ice concentration, gray-level co-occurrence matrix textures, polarization ratio, backscatter coefficients and intensity data.…”

Analysis and Classification of SAR Textures Using Information Theory

“…In higher resolution image classification activities, it is necessary to obtain more granular information from the data by extracting local characteristics such as scale and orientation. In this scenario, techniques such as Fourier power spectrum [25], random fields [26], Gabor filter [27] and wavelet transform [28] are usually applied.…”