Cloud Detection Algorithm for LandSat 8 Image Using Multispectral Rules and Spatial Variability

Man, Duc Chuc; Luu, Viet Hung; Hoang, Van Thang; Bui, Quynh; Nguyen, Tri-Thanh

doi:10.1007/978-3-319-11680-8_20

Cited by 2 publications

(1 citation statement)

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“…Most of modern methods of cloud zone detection in satellite images can be divided into three groups. The algorithms of the first group are based on calculations connected with certain circuit groups of multispectral images devoted to indices and characteristics and also decision functions applied to them [9,10,14,20]. They include the construction and analysis of histograms, threshold determination, as well as the analysis of deviations, etc.…”

Section: Introductionmentioning

confidence: 99%

Selection of Satellite Image Series for the Determination of Forest Pathology Dynamics Taking Into Account Cloud Coverage and Image Distortions Based on the Data Obtained from the Key Point Detector

Трубаков¹,

Trubakov²,

Трубаков³

et al. 2019

GraphiCon'2019 Proceedings. Volume 2

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Remote sensing of the earth and monitoring of various phenomena have been and still remain an important task for solving various problems. One of them is the forest pathology dynamics determining. Assuming its dependence on various factors forest pathology can be either short-term or long-term. Sometimes it is necessary to analyze satellite images within a period of several years in order to determine the dynamics of forest pathology. So it is connected with some special aspects and makes such analysis in manual mode impossible. At the same time automated methods face the problem of identifying a series of suitable images even though they are not covered by clouds, shadows, turbulence and other distortions. Classical methods of nebulosity determination based either on neural network or decision functions do not always give an acceptable result, because the cloud coverage by itself can be either of cirrus intortus type or insignificant within the image, but in case of cloudiness it can be the reason for wrong analysis of the area under examination. The article proposes a new approach for the analysis and selection of images based on key point detectors connected neither with cloudiness determination nor distorted area identification, but with the extraction of suitable images eliminating those that by their characteristics are unfit for forest pathology determination. Experiments have shown that the accuracy of this approach is higher than of currently used method in GIS, which is based on cloud detector.

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Section: Introductionmentioning

confidence: 99%