River ice monitoring and change detection with multi-spectral and SAR images: application over yellow river

Abstract

Spatially detailed characterization of the distribution amount and timing of river ice are important for identifying and predicting potential ice hazards. In this paper, we present an asynchronous river ice extraction and change detection method using multi-temporal SAR image and multi-spectral image. River channel information is a strong prior knowledge for ice detection and analysis. Therefore a river channel extraction algorithm on multi-spectral image based on sparse reconstruction is proposed and adopted in our method. The extracted river channel is used as prior information to effectively eliminate most interference regions on the shore. Then an adaptive threshold segmentation method is adopted to accurately detect river ice regions in SAR image. Fuzzy C-means clustering is used to segment river ice using the infrared bands of multi-spectral image, considering temperature can provide significant information to discriminate ice, water and shore. Finally, change analysis is done based on the ice extractions results of two kinds of images. The proposed method is applied on the Yellow River ice monitoring and experiments demonstrated that this straightforward approach works well with both SAR image and multi-spectral image.