Optical remote sensing cloud detection based on random forest only using the visible light and near-infrared image bands

Yao, Xudong; Guo, Qing; Liu, An; Shi, Lu

doi:10.1080/22797254.2021.2025433

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…Ma et al 35 propose a fully CNN ensemble learning model by combining U-Net and FCN-8sample networks to exploit spectral and spatial information to a larger extent. Yao et al 36 focus on the issue of small satellites with limited spectrum bands and presents the Random Forest Cloud Detection (RFCD) method that uses RGB and Near-Infrared data only. Pu et al 37 fuse the self-attention module and spatial pyramidal pooling module for high-precision cloud detection.…”

Section: Ai-based Image Processingmentioning

confidence: 99%

Data pipeline of a multi-spectral satellite experiment for object detection and artificial intelligence-based processing

Müller,

Swami,

Haser

et al. 2023

Automatic Target Recognition XXXIII

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Recent developments in the military domain introduce the need to detect and track hypersonic glide vehicles in Earth's atmosphere.The Multispectral Object Sensing by Artificial Intelligence-processed Cameras (MOSAIC) experiment is part of the small-satellite ATHENE-1 of the Universität der Bundeswehr München. The primary scientific objective of MOSAIC is to demonstrate reliable detection, identification a nd t racking o f h ypersonic g lide v ehicles using primarily a cooled infrared camera and complementary a visual camera. To cope with a large amount of data from both high-resolution cameras in real-time, state-of-the-art computer vision on-board processing methods are used for detection and tracking. The secondary scientific objective is to investigate the efficiency and reliability of Artificial Intelligence (AI) based image processing algorithms and data compression for space a pplications. This is of particular importance given the high volumes and rates of data. The application of such algorithms requires a reliable and resource-efficient On -Board Co mputer (O BC) th at ca n wi thstand th e ha rsh sp ace environment. The approach outlined in this paper envisions a dedicated OBC to manage the AI-based experiments of the satellite, called the Artificial Intelligence capable On-Board Computer ( AI-OBC). The AI-OBC includes multiple hardware-based AI accelerators to meet the computational requirements and ensure real-time processing for object detection and tracking. This paper describes the structure of the data processing pipeline and includes the AI-OBC architecture with its connections to both the cameras and the platform's OBC. Further, the study discusses the training and validation steps of the intended use-cases.

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Section: Ai-based Image Processingmentioning

confidence: 99%

Data pipeline of a multi-spectral satellite experiment for object detection and artificial intelligence-based processing

Müller,

Swami,

Haser

et al. 2023

Automatic Target Recognition XXXIII

View full text Add to dashboard Cite

show abstract

“…There are two reasons for only relying on four bands; firstly, these bands are commonly available in almost any multispectral satellite imagery (including commercial satellite imagery), thus it will ensure that our model can also be utilized with satellites other than Landsat-8 and Sentinel-2, and secondly, these bands show significant response for cloud pixels (Yao et al, 2022).…”

Section: Study Areamentioning

confidence: 99%

Detection of Clouds in Medium-Resolution Satellite Imagery Using Deep Convolutional Neural Nets

Hasan

Witharana²,

Udawalpola³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Cloud detection is an inextricable pre-processing step in remote sensing image analysis workflows. Most of the traditional rule-based and machine-learning-based algorithms utilize low-level features of the clouds and classify individual cloud pixels based on their spectral signatures. Cloud detection using such approaches can be challenging due to a multitude of factors including harsh lighting conditions, the presence of thin clouds, the context of surrounding pixels, and complex spatial patterns. In recent studies, deep convolutional neural networks (CNNs) have shown outstanding results in the computer vision domain. These methods are practiced for better capturing the texture, shape as well as context of images. In this study, we propose a deep learning CNN approach to detect cloud pixels from medium-resolution satellite imagery. The proposed CNN accounts for both the low-level features, such as color and texture information as well as high-level features extracted from successive convolutions of the input image. We prepared a cloud-pixel dataset of approximately 7273 randomly sampled 320 by 320 pixels image patches taken from a total of 121 Landsat-8 (30m) and Sentinel-2 (20m) image scenes. These satellite images come with cloud masks. From the available data channels, only blue, green, red, and NIR bands are fed into the model. The CNN model was trained on 5300 image patches and validated on 1973 independent image patches. As the final output from our model, we extract a binary mask of cloud pixels and non-cloud pixels. The results are benchmarked against established cloud detection methods using standard accuracy metrics.

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“…algorithm. The first type of algorithm is mainly based on the spectral features, brightness, texture features, and geometric features of clouds, and formulates thresholds or rules by analyzing the spectral differences between clouds and the surface, so as to achieve cloud extraction [2][3][4][5][6][7][8] . Due to its simple principle and fast calculation speed, this kind of method has been widely used in the business cloud detection of various remote sensing images, such as the APOLLO algorithm and CLAVR algorithm [9][10] for AVHRR data cloud detection, Fmask algorithm for Landsat 4-8 data cloud detection [11][12][13] Multi-feature integrated cloud detection algorithm developed for Gaofen-1, etc.…”

Section: Introductionmentioning

confidence: 99%

A brief analysis of remote sensing image cloud extraction method based on deep learning

Lin,

Gai

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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Optical remote sensing cloud detection based on random forest only using the visible light and near-infrared image bands

Cited by 11 publications

References 27 publications

Data pipeline of a multi-spectral satellite experiment for object detection and artificial intelligence-based processing

Data pipeline of a multi-spectral satellite experiment for object detection and artificial intelligence-based processing

Detection of Clouds in Medium-Resolution Satellite Imagery Using Deep Convolutional Neural Nets

A brief analysis of remote sensing image cloud extraction method based on deep learning

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