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Remote sensing can present the latest land cover information in an efficient manner, but the land cover mapping that has been widely carried out still uses optical imagery. Indonesia is a tropical country, so it is likely to be disrupted by cloud cover throughout the year. To solve this problem can use radar imagery. Radar images use microwaves that can penetrate clouds. But behind these advantages, radar images have noise in the form of black and white spots or salt and papper which can affect the results of processing when done on pixel basis. Therefore, it is necessary to extract information on radar images that do not only consider pixel values, namely object-based classification. This study aims to determine the best segmentation to map land cover. The second objective is to know the accuracy value of the segment produced. This research was conducted using a radar image, namely Citra Sentinel-1A with 10mx10m resolution. The segmentation process carried out using a multiresolution segmentation algorithm. Based on the results of the study, the best segmentation has an input channel parameter weight of 1, 0.5, 1, output parameter weight 25, shape parameter weight 0.3 and compactness parameter weight 0.9. The value of segmentation accuracy produced by considering five parameters in the shape of oversegmentation (OSeg), undersegmentation (USeg), root mean square error (D), area fit index (AFI), and quality rate (Qr) is 57%. Low accuracy value because radar images focus on object morphology in the shape of altitude and surface conditions. Whereas in a land cover the object's morphology can vary and surface roughness can vary.
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