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

## TL;DR

This paper introduces Cloud-Net, a deep learning-based end-to-end algorithm using Fully Convolutional Networks for accurate cloud detection in Landsat 8 satellite imagery, outperforming existing methods.

## Contribution

The paper presents Cloud-Net, a novel deep learning model that effectively detects clouds in Landsat 8 images without complex pre-processing, improving accuracy over state-of-the-art methods.

## Key findings

- Outperforms existing methods by 8.7% in Jaccard Index
- Uses a Fully Convolutional Network for global and local feature capture
- Requires no complicated pre-processing steps

## 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.