Autonomous cloud detection for remote sensing images using convolutional neural network

Abstract:

Aiming at realizing the autonomous cloud detection of remote sensing satellites for remote sensing images, improving the efficiency of autonomous target recognition, and avoiding the loss of key target information in remote sensing images with large cloud coverage that may bring about unnecessary waste of computing resources to subsequent algorithm processing, a cloud detection method was proposed based on convolutional neural network, which can achieve autonomous cloud detection with high accuracy. Firstly, a convolutional neural network was built based on the requirements of the task and the characteristics of the remote sensing image. Secondly, a large number of manually labeled remote sensing images were used to train the network, which was adjusted according to the training result until it reached a certain classification accuracy for the test image set. Finally, the captured remote sensing image was divided into hundreds of sub-images based on size, and the pre-trained network was used to predict whether the sub-images were covered by clouds. By analyzing the prediction results of all sub-images, the cloud coverage information of the original image was obtained. Taking the Landsat remote sensing images as the test object, results show that the proposed method achieved 95.3% for cloud coverage detection accuracy, 97.8% for no-cloud coverage detection accuracy, misjudgment rate of 2.58%, missing rate of 0.90%, and overall accuracy of 97.9%. With the implementation of a simple structured network and parallel computing technology, the method can be used for real-time space missions with guaranteed autonomy and robustness. The achievement of this work will provide a foundation for real-time orbit applications based on remote sensing images.