# Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1810.05782 · HTML: https://mlsanity.com/paper/1810.05782/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2018.

## Built on by (most cited first, 17 of 17)

- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [Segment anything, from space?](https://mlsanity.com/paper/2304.13000/) (2023)
- [Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art](https://mlsanity.com/paper/2111.03260/) (2021)
- [Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2401.15105/) (2024)
- [Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines](https://mlsanity.com/paper/2302.08270/) (2023)
- [Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector](https://mlsanity.com/paper/2112.01723/) (2021)
- [CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features](https://mlsanity.com/paper/2306.07186/) (2023)
- [Self-Configuring nnU-Nets Detect Clouds in Satellite Images](https://mlsanity.com/paper/2210.13659/) (2022)
- [Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection](https://mlsanity.com/paper/2306.09886/) (2023)
- [BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery](https://mlsanity.com/paper/2402.13918/) (2024)
- [Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels](https://mlsanity.com/paper/2307.07281/) (2023)
- [Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection](https://mlsanity.com/paper/2309.02150/) (2023)
- [MLMT-CNN for Object Detection and Segmentation in Multi-layer and Multi-spectral Images](https://mlsanity.com/paper/2407.14473/) (2024)
- [Multiresolution Fully Convolutional Networks to detect Clouds and Snow through Optical Satellite Images](https://mlsanity.com/paper/2201.02350/) (2022)
- [Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images](https://mlsanity.com/paper/2306.14515/) (2023)
- [Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding](https://mlsanity.com/paper/2311.05198/) (2023)
- [Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images](https://mlsanity.com/paper/2609.21629/) (2026)

## Abstract (arXiv)

This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.