This paper presents a new algorithm for SAR sea ice image segmentation by combining the speckle reduction region growing（ SRRG） model with region-level MRF models. Region-level MRF-based algorithms are widely used in SAR sea ice image segmentation. The over-segmentation degree of the initial areas and the localization of the target edges significantly affect the c omputational complexity and segmentation accuracy of region-level MRF-based algorithms. Serious over-segmentation leads to increased computational complexity

whereas accurate target edge position is beneficial to obtaining precise segmentation results. Given that existing region-level MRF-based segmentation algorithms are inadequate for determining the effects of speckle noise and the relationships between regions

the segmentation process usually needs more time

and the probability of false segmentation increases. In other words

the segmentation accuracy is usually low. Hence

this paper proposes a new segmentation algorithm based on region-level MRF models combined with a speckle reduction region growing model（ SRRG-MRF）. This proposed SRRG-MRF can effectively reduce the interference of the speckle noise and significantly improve segmentation accuracy by fully considering the similarity between adjacent regions. The SRRG model includes two parts: construction of an image speckle reduction r egional representation and region growing based on the gray similarity of adjacent regions. For the former

speckle noise is initially suppressed using the proposed Speckle Reduction Bilateral Filter（ SRBF） algorithm. The region adjacency graph is then built to obtain regional representation of the image based on watershed transform. The SRBF algorithm can thus effectively inhibit the w atershed over-segmentation and achieve accurate positioning of the target edges. For the latter

the gray similarity of adjacent r egions can initially describe the local characteristics of the image more accurately than the edge strength. The gray similarity p enalty function of the adjacent areas is then introduced into the region MRF model based on the Gamma distribution. The region merger guideline is subsequently defined for region growing by calculating the energy difference between adjacent regions. Combining the SRRG region model with the MRF model can significantly reduce the optimization search space

prevent MRF segmentation optimization into localminimum

and reduce false segmentation to obtain accurate results. The proposed segmentation algorithm was evaluated using several synthetic SAR sea ice images corrupted with various levels of speckle noise and the real SAR sea ice images obtained by RADARSAT-2 and SIR-C

respectively. The overall segmentation accuracy and κ coefficient were used for algorithm evaluation. First

the experiment compared the number of watershed segmentation regions without filter processing and five kinds of filter processing

such as traditional Bilateral Filtering

Enhanced Lee

SRAD

and SRBF algorithm. Results showed that the SRBF algorithm is more effective in inhibiting over-segmentation and can obtain the accurate position of the target edges. The experiment then compared the proposed algorithm with the existing region-level MRF-based algorithms

namely

RMRF

IRGS

and EPR-MRF. The new segmentation algorithm substantially improved the segmentation accuracy and κ coefficient compared with the existing regionlevel MRF-based algorithms. The testing results demonstrated that the proposed algorithm is an effective and feasible method for SAR sea ice image segmentation. On one hand

SRRG-MRF can effectively reduce the interference of the speckle noise to inhibit watershed over-segmentation and achieve accurate positioning of the target edges. On other hand

SRRG-MRF can reduce the optimization search space

prevent MRF segmentation optimization into local minimum

and r educe false segmentation to obtain accurate results.