Multi-scale lightweight cloud detection method for optical remote sensing images
In order to solve the current problem of low accuracy,high misjudgment,and omission of cloud detection in optical remote sensing images.In this paper,a multi-scale lightweight convolutional neural network is proposed to realize the cloud detection task by adopting the lightweight LM-DeeplabV3+framework proposed in this paper and combining with the ZY-3 satellite image data to extract features with a larger sensing field and multiple spatial scales.Firstly,this paper presents an improved lightweight L-MobileNetV2 convolutional neural network as the backbone model.Compared with the traditional Xception model,this lightweight approach greatly reduces the number of parameters,which improves the training speed and inference time of the model.Second,to make up for the deficiency in capturing the details of image features after the lightweight of the backbone model,a CA(Coordinate Attention)coordinate attention module is introduced after the backbone network,which can pay better attention to the relationship between different spatial locations in the feature map,thus providing a more in-depth understanding of the relative positions of the clouds in the image,and helping to improve the model's understanding of the image features,which helps to improve the model's ability to understand the image features,and thus improve the accuracy of cloud detection.Meanwhile,this paper also proposes a PPM-ASPP module,which can extract features at multiple different scales to better adapt to different types and sizes of clouds.The evaluation of the experimental results shows that the remote sensing image cloud detection method used in this paper has less training time,low misjudgment,high cloud detection accuracy,and is suitable for high-resolution optical remote sensing image cloud detection tasks.
万方数据
维普
