Cloud detection algorithm for remote sensing images based on semantic-guided and adaptive convolution
Cloud detection of remote sensing satellite data is a crucial component in the processing of remote sensing images.To address the issue of low accuracy in detecting broken-clouds and thin-clouds,this paper proposes a novel cloud detection method that utilizes high-order semantic-guided decoding and adaptive convolutional encoding.The method leverages the spatial distribution relationship between the main cloud and broken-clouds by introducing an adaptive convolutional encoder to extract correlation information between the main cloud clusters.A high-order semantic-guided decoding module is then utilized to decode semantic features,thus restoring high-resolution cloud mask images.Moreover,a dynamic fusion loss function is designed to calculate the weight by dynamically computing the missed and wrong pixels in the prediction,guiding the network to focus on broken-clouds and thin-clouds,features,thereby enhancing the overall accuracy.Experimental results demonstrate that the proposed algorithm achieves an accuracy of over 96.5%and an intersection over union of over 88.1%,effectively detecting broken-clouds and thin-clouds.
万方数据
维普
