Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification

Johannes Lohse,Wolfgang Dierking,Anthony P Doulgeris

doi:10.3390/rs13040552

Abstract

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice.

Highlights

Synthetic aperture radar (SAR) is a primary tool for monitoring of sea ice conditions in the polar regions [1,2,3]
We begin by comparing the influence of incident angle (IA) on grey level co-occurrence matrix (GLCM) texture features computed from intensity in dB against GLCM texture features computed from linear intensity
GLCM texture features extracted from the S1 extra-wide swath (EW) ground-range detected format at medium resolution (GRDM) product, and we assessed their potential to be included in IA-sensitive sea ice classification

Summary

Introduction

Synthetic aperture radar (SAR) is a primary tool for monitoring of sea ice conditions in the polar regions [1,2,3]. The analysis and interpretation of the SAR images and the production of ice charts is at present carried out manually and subject to the expertise of the individual ice analyst [5,6]. While timeliness of ice charts is a critical requirement, the manual image analysis is a time-consuming process [7]. In combination with an increasing volume of available SAR imagery, this underlines the need for automated or computer-assisted classification of sea ice. The backscatter signature of sea ice in radar images, depends on a variety of different factors, including sea ice, environmental, and radar parameters [8,9]. Despite multiple efforts and various approaches, robust and automated classification of ice types remains a challenging task [3]

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