Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network

Abstract

Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research.

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

“…In addition, refinements were done on small patch masks rather than large masks of the entire Landsat 8 scenes. In another work, the authors of Cloud-AttU [51] employed a UNet model that is enriched with a specific attention module in its skip connections. Multiple attention modules enabled the model to learn proper features by paying attention to the most relevant locations in input training images or feature maps.…”

Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations

“…Studies have demonstrated the potential for CNNs to enhance cloud detection using high-resolution (<100 m) LEO satellites such as Landsat-8. [40][41][42][43][44] One study had overall cloud detection accuracy of 97.05% from Landsat-8 images and provided examples of successful identification of clouds over snow and ice. 42 Some CNNs also distinguish between optically thin and thick clouds, 40,41 which would allow for special treatment of areas where the surface is visible.…”

Seeing Through Clouds in Satellite Images

“…Single-scene algorithms can utilize the spatial context through deep learning approaches. Deep cloud detection largely relies on the U-net architecture and has been shown to outperform the Fmask algorithm, [25,117,102]. The deep learning approach requires a large annotated data set.…”