Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery

Code

• github.com/dfrisinghelli/pysegcnn
  pytorch★ 2
• github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset
  none★ 0
• github.com/SorourMo/Cloud-Net-A-semantic-segmentation-CNN-for-cloud-detection
  tf★ 0

Abstract

Cloud detection in satellite images is an important first-step in many remote sensing applications. This problem is more challenging when only a limited number of spectral bands are available. To address this problem, a deep learning-based algorithm is proposed in this paper. This algorithm consists of a Fully Convolutional Network (FCN) that is trained by multiple patches of Landsat 8 images. This network, which is called Cloud-Net, is capable of capturing global and local cloud features in an image using its convolutional blocks. Since the proposed method is an end-to-end solution, no complicated pre-processing step is required. Our experimental results prove that the proposed method outperforms the state-of-the-art method over a benchmark dataset by 8.7\% in Jaccard Index.

Tasks

Benchmark Results

Reproductions