River Channel Extraction From SAR Images by Combining Gray and Morphological Features

In remote sensing images acquired by the synthetic aperture radar (SAR), the backscattering coefficient is the function of the gray level. Since the gray of the pixels in the water area is relatively low, the gray level is commonly regarded as a valid feature for river channel identification and extraction. However, the gray threshold method is challenging mostly due to the speckles and false objects with similar gray value to the interest objects. Combining gray and morphological features, a novel multistage method for river channel extraction is proposed in this paper. First, the gray threshold-based image segmentation method is applied for initially removing the background noises. Next, a novel morphological model is proposed for identifying the river channel. After extracting the rough area of the river channel, the gray threshold-based image segmentation is reused for pruning results. Finally, the morphological filter is employed for correcting results. The benefit of the proposed method lies in its ability for properly combining the gray and morphological features, and the advantages of these features are fully exploited. The proposed method was evaluated both objectively and subjectively by utilizing a series of SAR images and quantified criterions. For the overall dataset, the missed detection rate, false alarm rate, and the correctness were statistically calculated, respectively. All experimental results proved that in comparision to the classic Otsu gray threshold method and its updated editions, the proposed approach is fast, robust, and effective for river channel extraction.