CLOUD AND CLOUD SHADOW MASKING USING MULTI-TEMPORAL CLOUD MASKING ALGORITHM IN TROPICAL ENVIRONMENTAL

A cloud masking approach based on multi-temporal satellite images is proposed. The basic idea of this approach is to detect cloud and cloud shadow by using the difference reflectance values between clear pixels and cloud and cloud shadow contaminated pixels. Several bands of satellite image which have big difference values are selected for developing Multi-temporal Cloud Masking (MCM) algorithm. Some experimental analyses are conducted by using Landsat-8 images. Band 3 and band 4 are selected because they can distinguish between cloud and non cloud. Afterwards, band 5 and band 6 are used to distinguish between cloud shadow and clear. The results show that the MCM algorithm can detect cloud and cloud shadow appropriately. Moreover, qualitative and quantitative assessments are conducted using visual inspections and confusion matrix, respectively, to evaluate the reliability of this algorithm. Comparison between this algorithm and QA band are conducted to prove the reliability of the approach. The results show that MCM better than QA band and the accuracy of the results are very high.

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AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-475-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 475-479 ◽

Cited By ~ 2

Author(s):

Nicolas Champion

Keyword(s):

Time Series ◽

Satellite Images ◽

Satellite Image ◽

Region Growing ◽

Shadow Detection ◽

The Other ◽

Landsat 8 ◽

Cloud Detection ◽

Large Area ◽

The Difference

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.

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AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-475-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 475-479 ◽

Cited By ~ 3

Author(s):

Nicolas Champion

Keyword(s):

Time Series ◽

Satellite Images ◽

Satellite Image ◽

Region Growing ◽

Shadow Detection ◽

The Other ◽

Landsat 8 ◽

Cloud Detection ◽

Large Area ◽

The Difference

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.

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Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

Journal of Science Natural Science ◽

10.18173/2354-1059.2021-0021 ◽

2021 ◽

Vol 66 (1) ◽

pp. 175-187

Author(s):

Duong Phung Thai ◽

Son Ton

Keyword(s):

Satellite Images ◽

Satellite Image ◽

Image Interpretation ◽

Image Data ◽

Vegetation Coverage ◽

Landsat 8 ◽

Mangrove Forests ◽

Forest Change ◽

Shrimp Ponds ◽

Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

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Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM

Advances in Geospatial Technologies - Handbook of Research on Geographic Information Systems Applications and Advancements ◽

10.4018/978-1-5225-0937-0.ch015 ◽

2017 ◽

pp. 387-406

Author(s):

Ali Ben Abbes ◽

Imed Riadh Farah

Keyword(s):

Land Use ◽

Land Use Changes ◽

Satellite Image ◽

Remotely Sensed ◽

High Temporal Resolution ◽

Remotely Sensed Data ◽

Vegetation Monitoring ◽

Area Of Interest ◽

Multi Temporal ◽

The Difference

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).

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Towards Sub-Pixel Automatic Geometric Corrections of Very-High Resolution Panchromatic Satellite Data of Urban Areas

Remote Sensing ◽

10.3390/rs11091097 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1097 ◽

Cited By ~ 1

Author(s):

Aleš Marsetič ◽

Peter Pehani

Keyword(s):

Feature Extraction ◽

High Resolution ◽

Urban Areas ◽

Satellite Images ◽

Reference Data ◽

Geometric Model ◽

Control Point ◽

Satellite Image ◽

Extraction Algorithm ◽

Very High

This paper presents an automatic procedure for the geometric corrections of very-high resolution (VHR) optical panchromatic satellite images. The procedure is composed of three steps: an automatic ground control point (GCP) extraction algorithm that matches the linear features that were extracted from the satellite image and reference data; a geometric model that applies a rational function model; and, the orthorectification procedure. Accurate geometric corrections can only be achieved if GCPs are employed to precisely correct the geometric biases of images. Due to the high resolution and the varied acquisition geometry of images, we propose a fast, segmentation based method for feature extraction. The research focuses on densely populated urban areas, which are very challenging in terms of feature extraction and matching. The proposed algorithm is capable of achieving results with a root mean square error of approximately one pixel or better, on a test set of 14 panchromatic Pléiades images. The procedure is robust and it performs well in urban areas, even for images with high off-nadir angles.

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Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽

10.3390/proceedings2231430 ◽

2018 ◽

Vol 2 (23) ◽

pp. 1430

Author(s):

V. M. Fernández-Pacheco ◽

C. A. López-Sánchez ◽

E. Álvarez-Álvarez ◽

M. J. Suárez López ◽

L. García-Expósito ◽

...

Keyword(s):

Remote Sensing ◽

Air Pollutants ◽

Satellite Images ◽

Satellite Image ◽

Remote Sensing Image ◽

Regional Studies ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Landsat 5 Tm ◽

Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

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Covid-19: Comparisons by Country and Implications for Future Pandemics

10.1101/2021.05.29.21258056 ◽

2021 ◽

Author(s):

Lewis E. Mehl-Madrona ◽

Francois Bricaire ◽

Adrian Cuyugan ◽

J. Barac ◽

Asadullah Parvaiz ◽

...

Keyword(s):

The United States ◽

Mortality Rates ◽

Social Structures ◽

Lower Mortality ◽

Vitamin D Status ◽

Northern European ◽

The Usa ◽

The Difference ◽

Very High ◽

Better Than

Background. We set out in this paper to compare Covid-19 results by country to better understand the factors leading to the differing results found internationally. Methods. We used publicly available large datasets to explore differences by the country for Covid-19 mortality statistics. We continuously challenged our projections with reality and numbers from countries around the world, allowing us to refine our models and better understand the progression of the epidemic. All our predictions and findings were discussed and validated from a clinical viewpoint. Results. While no lockdown resulted in higher mortality, the difference between strict lock-down and a lax lockdown was not terribly different and favored lax lockdown. Only one of the top 44 countries had long and strict restrictions. Strict restrictions were more common in the worst-performing countries in terms of Covid mortality. The United States had the largest economic growth coupled with the largest rate of mortality. Those who did well economically had lower mortality and less pressure on their population. Yet they had less mortality than average and less than their neighbors. Conclusions. Countries with the least restrictions fared best economically. Some of them fared well in terms of mortality, even better than neighboring countries with similar social structures and more severe restrictions. The mortality rates in the USA, however, appeared to suffer from very high obesity rates. Norway and the northern European countries have less strict restrictions from the rest of Europe and had lower mortality rates. COVID-19 mortality was associated with vitamin D status.

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STUDY ON MOSAIC AND UNIFORM COLOR METHOD OF SATELLITE IMAGE FUSION IN LARGE SREA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1099-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1099-1102

Author(s):

S. Liu ◽

H. Li ◽

X. Wang ◽

L. Guo ◽

R. Wang

Keyword(s):

Information Entropy ◽

Satellite Images ◽

Satellite Image ◽

Image Data ◽

Texture Features ◽

Color Difference ◽

Large Area ◽

Color Mapping ◽

Multi Temporal ◽

Mosaic Images

Due to the improvement of satellite radiometric resolution and the color difference for multi-temporal satellite remote sensing images and the large amount of satellite image data, how to complete the mosaic and uniform color process of satellite images is always an important problem in image processing. First of all using the bundle uniform color method and least squares mosaic method of GXL and the dodging function, the uniform transition of color and brightness can be realized in large area and multi-temporal satellite images. Secondly, using Color Mapping software to color mosaic images of 16bit to mosaic images of 8bit based on uniform color method with low resolution reference images. At last, qualitative and quantitative analytical methods are used respectively to analyse and evaluate satellite image after mosaic and uniformity coloring. The test reflects the correlation of mosaic images before and after coloring is higher than 95&thinsp;% and image information entropy increases, texture features are enhanced which have been proved by calculation of quantitative indexes such as correlation coefficient and information entropy. Satellite image mosaic and color processing in large area has been well implemented.

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Structural interpretation of lineaments using satellite image processing: A case study in the vicinity of the Charvak reservoir

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2020-2-26-436-442 ◽

2020 ◽

Vol 26 (2) ◽

pp. 436-442

Author(s):

Lola Sichugova ◽

Dilbarkhon Fazilova

Keyword(s):

Water Level ◽

Satellite Images ◽

Satellite Image ◽

Water Reservoir ◽

Water Level Fluctuations ◽

Landsat 8 ◽

Base Line ◽

The North ◽

Automated Method ◽

Density Maps

This work presents the results of lineaments interpretation using the automated method of the satellite images in the territory of the Charvak water reservoir in Uzbekistan. Tectonic and local (water impoundment in Charvak reservoir) features of the region deformation were determined on base LINE algorithm in software PCI Geomatica. The thematic map with the geospatial arrangement of lineaments was constructed on base of satellite images LANDSAT-8 processing. We concluded that water level fluctuations have a greater influence on the appearance of the lineaments structure than periods of water filling and downstream in the reservoir. Lineament density maps showed dominantly increased density towards the north-southern direction is due to tectonic features of the region and the west-eastern direction is due to water level fluctuations in the reservoir. The lineaments density maps for summer-autumn periods showed the faults arising from water level fluctuations only. Winter-spring period affected with high influence of the seasonal (snow pack, rainfall) processes as well.

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