# High-Resolution Cloud Detection Network

arXiv: https://arxiv.org/abs/2407.07365 · HTML: https://mlsanity.com/paper/2407.07365/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Siamese Network for RGB-D Salient Object Detection and Beyond](https://mlsanity.com/paper/2008.12134/) (2020)
- [CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2306.04300/) (2023)
- [Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery](https://mlsanity.com/paper/1901.10077/) (2019)

## Built on by (most cited first, 8 of 8)

- [FlexiMo: A Flexible Remote Sensing Foundation Model](https://mlsanity.com/paper/2503.23844/) (2025)
- [Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2411.13127/) (2024)
- [Dynamic Dictionary Learning for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2503.06683/) (2025)
- [Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation](https://mlsanity.com/paper/2412.06664/) (2024)
- [CD-Mamba: Cloud detection with long-range spatial dependency modeling](https://mlsanity.com/paper/2509.04729/) (2025)
- [MSCloudCAM: Multi-Scale Context Adaptation with Convolutional Cross-Attention for Multispectral Cloud Segmentation](https://mlsanity.com/paper/2510.10802/) (2025)
- [CloudMatch: Weak-to-Strong Consistency Learning for Semi-Supervised Cloud Detection](https://mlsanity.com/paper/2601.03528/) (2026)
- [TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection](https://mlsanity.com/paper/2602.21905/) (2026)

## Abstract (arXiv)

The complexity of clouds, particularly in terms of texture detail at high resolutions, has not been well explored by most existing cloud detection networks. This paper introduces the High-Resolution Cloud Detection Network (HR-cloud-Net), which utilizes a hierarchical high-resolution integration approach. HR-cloud-Net integrates a high-resolution representation module, layer-wise cascaded feature fusion module, and multi-resolution pyramid pooling module to effectively capture complex cloud features. This architecture preserves detailed cloud texture information while facilitating feature exchange across different resolutions, thereby enhancing overall performance in cloud detection. Additionally, a novel approach is introduced wherein a student view, trained on noisy augmented images, is supervised by a teacher view processing normal images. This setup enables the student to learn from cleaner supervisions provided by the teacher, leading to improved performance. Extensive evaluations on three optical satellite image cloud detection datasets validate the superior performance of HR-cloud-Net compared to existing methods.The source code is available at urlhttps://github.com/kunzhan/HR-cloud-Net.