An improved fuzzy logic segmentation of sea ice, clouds, and ocean in remotely sensed arctic imagery

Abstract The accurate segmentation of sea ice from cloud and from cloud-free ocean in polar AVHRR imagery is important for many scientific applications (e.g., sea ice-albedo feedback mechanisms, heat exchange between ocean and atmosphere in polar regions; studies of the stability of surface water in polar regions). Unfortunately, it is a difficult task complicated by the common visible reflectance characteristics of sea ice and cloud. Moreover, AVHRR Channel 3 data historically have been contaminated by highly variable sensor noise which generally has hampered their use in the classification of polar scenes. Likewise, polar scenes often contain pixels with mixed classes (e.g., sea ice and cloud). This article uses a combination of fuzzy logic classification methods, noise reduction in AVHRR Channel 3 data using Wiener filtering methods (Simpson and Yhann, 1994), and a physically motivated rule base which makes effective use of the Wiener filtered Channel 3 data to more accurately segment polar imagery. The new method's improved classification skill compared to more traditional methods, as well as its regional independence, is demonstrated. The algorithm is computationally efficient and hence is suitable for analyzing the large volumes of polar imagery needed in many global change studies.