In the synthetic aperture radar (SAR) images, there are many complex background problems, such as speckle noise, close contact of floating ice in drift-ice area, and more small pieces of ice. Here, we describe a SAR flow ice separation algorithm based on background-suppression saliency detection. This algorithm obtains a preliminary saliency map by learning the random forest regression based on the saliency detection of images. Then the image region is constructed by using super pixels. Additionally, discrete Fourier transformation is performed, after which the frequency domain features of the region are extracted, and the chi-squared distance is calculated. The proposed algorithm then suppresses the boundary background to generate a background-suppression module diagram, followed by fusion of the two-stage graph to obtain the enhanced saliency graph. We evaluate the proposed algorithm and compare its performance against seven saliency algorithms and three sea-ice-segmentation methods using a SAR sea-ice dataset. The results show that the proposed algorithm can effectively detect isolated ice floes and suppress background areas.