Contextual Soft Classification Approaches for Crops Identification Using Multi-sensory Remote Sensing Data: Machine Learning Perspective for Satellite Images

Remote sensing data are widely used in coastal zones monitoring, since they provide radiometric information with the possibility to automate the data processing. Due to the lack of high resolution satellite images in free access using the medium resolution satellite images is widespread. The study is dedicated to the development of a coastline detection method based on medium resolution satellite images. It is proposed to use a semi-automatic method based on uncontrolled classification of a mid-resolution image by water indices, followed by expert refinement of classes and the use of machine learning methods. The shorelines of the eastern part of the Gulf of Finland have been extracted from Landsat 8 Level-2 image, using proposed method. The position accuracy of the generated shorelines has been analyzed using manually digitized shoreline from high-resolution image Resurs-P1 processing level 2А. The results showed that in the test areas the best output for extracting the coastline are given by the MNDWI with a fixed threshold value equal to zero. The Random Forest machine learning algorithm was used to refine the type of coastline, especially in wetlands where water indices showed poor accuracy. The study showed a significant increase in the position accuracy with the use of the algorithm. However, the accuracy of manually classified wetlands for training the model has a significant impact on the result.

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SURVEY OF THE PROBLEM OF EARTH REMOTE SENSING DATA IN ORDER TO INCREASE SATELLITE IMAGES DETAILS

Modern Technologies in Science and Education MTSE-2020, Vol.6 ◽

10.21667/978-5-6044782-4-0-189-196 ◽

2020 ◽

Author(s):

S.M. Larionov ◽

◽

A.E. Moskvitin ◽

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Remote Sensing Data ◽

Earth Remote Sensing ◽

Sensing Data

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Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020058 ◽

2021 ◽

Vol 10 (2) ◽

pp. 58

Author(s):

Muhammad Fawad Akbar Khan ◽

Khan Muhammad ◽

Shahid Bashir ◽

Shahab Ud Din ◽

Muhammad Hanif

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Learning Algorithms ◽

Remote Sensing Data ◽

Kappa Coefficient ◽

Machine Learning Algorithms ◽

Landsat 8 ◽

Sensing Data ◽

Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

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Crop Identification Based on Remote Sensing Data using Machine Learning Approaches for Fresno County, California

10.1109/bigdataservice52369.2021.00019 ◽

2021 ◽

Author(s):

Sunanda Das Suchi ◽

Aparna Menon ◽

Ashima Malik ◽

Jie Hu ◽

Jerry Gao

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Remote Sensing Data ◽

Learning Approaches ◽

Fresno County ◽

Sensing Data

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Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

Journal of Geography and Geology ◽

10.5539/jgg.v10n1p1 ◽

2017 ◽

Vol 10 (1) ◽

pp. 1 ◽

Cited By ~ 4

Author(s):

Clement Kwang ◽

Edward Matthew Osei Jnr ◽

Adwoa Sarpong Amoah

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Remote Sensing Data ◽

Unsupervised Classification ◽

Water Bodies ◽

Water Extraction ◽

Landsat 8 ◽

Sensing Data ◽

Optimal Classifier ◽

Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

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Summaries of certain spatial patterns retrieved from multidate remote-sensing data

Discrete Dynamics in Nature and Society ◽

10.1155/s1026022604402027 ◽

2004 ◽

Vol 2004 (2) ◽

pp. 287-300

Author(s):

Hema Nair

Keyword(s):

Remote Sensing ◽

Genetic Algorithms ◽

Fuzzy Logic ◽

Spatial Patterns ◽

Satellite Images ◽

Remote Sensing Data ◽

Geographic Area ◽

Image Mining ◽

Sensing Data

This paper presents an approach to describe patterns in remote-sensed images utilising fuzzy logic. The truth of a linguistic proposition such as Y isF can be determined for each pattern characterised by a tuple in the database, where Y is the pattern andFis a summary that applies to that pattern. This proposition is formulated in terms of primary quantitative measures, such as area, length, perimeter, and so forth, of the pattern. Fuzzy descriptions of linguistic summaries help to evaluate the degree to which a summary describes a pattern or object in the database. Techniques, such as clustering and genetic algorithms, are used to mine images. Image mining is a relatively new area of research. It is used to extract patterns from multidated satellite images of a geographic area.

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Monitoring of glaciers in the Polar Urals using remote sensing Data

E3S Web of Conferences ◽

10.1051/e3sconf/202021704003 ◽

2020 ◽

Vol 217 ◽

pp. 04003

Author(s):

Natalia Martynova ◽

Valentina Budarova ◽

Artem Sheremetinsky ◽

Nikita Mezentsev

Keyword(s):

Remote Sensing ◽

Technological Progress ◽

Satellite Images ◽

Research Question ◽

Remote Sensing Data ◽

Field Work ◽

Sources Of Information ◽

Sensing Data ◽

Polar Urals ◽

The Polar Urals

The development of technological progress provides more opportunities for indirect monitoring of changes in the environment. Remote sensing is one of The most accessible and reliable sources of information. In this work, we used satellite images from the Landsat family. The theoretical justification of the research question is given. The research methodology was developed. Collection and processing of satellite images for various time periods. A series of schematic maps based on remote sensing Data has been created. As a result of digitization of satellite images, 9 glacier contours were obtained by year. We determined the area of the Romantics glacier and found that it lost at least 60% of its original area. These studies were used to build a series of cartographic schemes that clearly show the reduction of the glacier area. It is concluded that the use of remote sensing allows you to solve problems, monitoring the object. The use of this method allows not only to save time for field work, but also material costs for expedition equipment and various equipment. This method can be tested on any objects.

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