Cloud detection using infrared atmospheric sounding interferometer observations by logistic regression

Luo, Tengling; Zhang, Weimin; Yu, Yi; Feng, Miao; Duan, Boheng; Xing, De

doi:10.1080/2150704x.2018.1553318

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…The logistic S-curve application model is based on firmly proven laws of nature [24][25][26][27]. The S-curve model represents the growth or decline of every system in interaction with its environment (its limited resources).…”

Section: Discussionmentioning

confidence: 99%

Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Zhang

Yang

2019

Forests

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Land cover monitoring is a major task for remote sensing. Comparing to traditional methods for forests monitoring which mostly use orthoimages from satellite or aircraft, there are very few researches use forest 3D canopy structure to monitor the forest growth. UAV aerial can be a novel and feasible platform to provide high resolution and more timely images that can be used to generate high resolution forest 3D canopy. In spring, the small forest is supposed to experience rapid growth. In this research, we used a small UAV to monitor campus forest growth in spring at 2days interval. Each time 140 images were acquired and the ground surface dense point cloud was reconstructed at high precision. Color indexes ExG (Excess Green) was used to extract the green canopy point. The segmented point cloud was triangulated using greedy projection triangulation method into a mesh and its area was calculated. Forest canopy growth was analyzed at 3 level: forest level, selected group level and individual tree level. Logistic curve was used to fit the time series canopy growth. Strong correlation was found R2 = 0.8517 at forest level, R2=0.9652 at selected group level and R2 = 0.9606 at individual tree level. Moreover, high correlation was found between canopy by observing these results, we can conclude that the ground 3D model can act as a useful data type as orthography to monitor the forest growth. Moreover the UAV aerial remote sensing has advantages at monitoring forest in periods when the ground vegetation is growing and changing fast.

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Section: Discussionmentioning

confidence: 99%

Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Zhang

Yang

2019

Forests

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“…which has been widely used in data mining and classification [42]. In the field of cloud detection, Luo [26] used the logistic regression method for IASI cloud detection, obtaining robust results for sea areas with a test accuracy of 97%. Equation ( 4) is the cost function of the logistic regression, which consists of two terms, the first term is the loss function, and the second term is the regular term:…”

Section: Spatial Matchingmentioning

confidence: 99%

“…These detection methods based on machine learning algorithms can be roughly divided into two categories according to the input. In the first category, satellite images with high resolution are used as input [24][25][26][27][28][29][30][31][32][33][34][35]. Many studies have achieved good results in cloud detection on satellite images after adjusting or changing some layers of the classical neural network (e.g., U-Net, VGG-16) [34,35] and other deep learning networks [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Cloud Detection from FY-4A’s Geostationary Interferometric Infrared Sounder Using Machine Learning Approaches

Zhang

et al. 2019

Remote Sensing

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FengYun-4A (FY-4A)’s Geostationary Interferometric Infrared Sounder (GIIRS) is the first hyperspectral infrared sounder on board a geostationary satellite, enabling the collection of infrared detection data with high temporal and spectral resolution. As clouds have complex spectral characteristics, and the retrieval of atmospheric profiles incorporating clouds is a significant problem, it is often necessary to undertake cloud detection before further processing procedures for cloud pixels when infrared hyperspectral data is entered into assimilation system. In this study, we proposed machine-learning-based cloud detection models using two kinds of GIIRS channel observation sets (689 channels and 38 channels) as features. Due to differences in surface cover and meteorological elements between land and sea, we chose logistic regression (lr) model for the land and extremely randomized tree (et) model for the sea respectively. Six hundred and eighty-nine channels models produced slightly higher performance (Heidke skill score (HSS) of 0.780 and false alarm rate (FAR) of 16.6% on land, HSS of 0.945 and FAR of 4.7% at sea) than 38 channels models (HSSof 0.741 and FAR of 17.7% on land, HSS of 0.912 and FAR of 7.1% at sea). By comparing visualized cloud detection results with the Himawari-8 Advanced Himawari Imager (AHI) cloud images, the proposed method has a good ability to identify clouds under circumstances such as typhoons, snow covered land, and bright broken clouds. In addition, compared with the collocated Advanced Geosynchronous Radiation Imager (AGRI)-GIIRS cloud detection method, the machine learning cloud detection method has a significant advantage in time cost. This method is not effective for the detection of partially cloudy GIIRS’s field of views, and there are limitations in the scope of spatial application.

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“…Machine learning techniques based on principal component analysis, artificial neural networks, or support vector machines, among others, have been extensively applied to identify clouds from high resolution satellite imagers [22], however, it is less common to find cloud detection algorithms based on hyperspectral infrared sounders. The existing ones are generally applied by selecting specific spectral channels and/or evaluating brightness temperature thresholds and brightness temperature differences [23][24][25][26], which can limit their applicability and require elaborate preliminary calibrations. Commonly, they are employed in conjunction with high spatial resolution imagers [24,27], which implies a relatively high computational cost due to the necessity to combine two different types of measurements.…”

Section: Introductionmentioning

confidence: 99%

Cirrus Cloud Identification from Airborne Far-Infrared and Mid-Infrared Spectra

et al. 2020

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Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far- and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm’s performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid- and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA’s 9th Earth Explorer.

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Cloud detection using infrared atmospheric sounding interferometer observations by logistic regression

Cited by 5 publications

References 25 publications

Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Cloud Detection from FY-4A’s Geostationary Interferometric Infrared Sounder Using Machine Learning Approaches

Cirrus Cloud Identification from Airborne Far-Infrared and Mid-Infrared Spectra

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