Cloud Detection in High-Resolution Multispectral Satellite Imagery Using Deep Learning
- 3.5k Downloads
Cloud detection in high-resolution satellite images is a critical step for many remote sensing applications, but also a challenge, as such images have limited spectral bands. The contribution of this paper is twofold: We present a dataset called CloudPeru as well as a methodology for cloud detection in multispectral satellite images (approximately 2.8 meters per pixel) using deep learning. We prove that an agile Convolutional Neural Network (CNN) is able to distinguish between non-clouds and different types of clouds, including thin and very small ones, and achieve a classification accuracy of 99.94%. Each image is subdivided into superpixels by the SLICO algorithm, which are then processed by the trained CNN. Finally, we obtain the cloud mask by applying a threshold of 0.5 on the probability map. The results are compared with manually annotated images, showing a Kappa coefficient of 0.944, which is higher than that of compared methods.
KeywordsCloud detection High-resolution Convolutional neural networks Deep learning
The authors would like to thank the National Commission for Aerospace Research and Development (CONIDA) and the National Institute of Research and Training in Telecommunications of the National University of Engineering (INICTEL-UNI) for the support provided.
- 7.Morales, G., Huamán, S., Telles, J.: Cloud detection for PERUSAT-1 imagery using spectral and texture descriptors, ANN and panchromatic fusion. In: Proceedings of the 3rd Brazilian Technology Symposium - Emerging Trends and Challenges in Technology (BTSym). Springer, Campinas (2018, in press)Google Scholar
- 8.Shi, M., Xie, F., Zi, Y., Yin, J.: Cloud detection of remote sensing images by deep learning. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 701–704. IEEE Press, Beijing (2016)Google Scholar
- 11.CloudPeru Dataset. http://didt.inictel-uni.edu.pe/dataset/CloudPeru.hdf5
- 12.He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034. IEEE Press, Vancouver (2015)Google Scholar
- 13.Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR), San Diego (2015)Google Scholar