scholarly journals GEOSPATIAL MACHINE LEARNING DATASETS STRUCTURING AND CLASSIFICATION TOOL: CASE STUDY FOR MAPPING LULC FROM RASAT SATELLITE IMAGES

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 39-46
Author(s):  
S. K. M. Abujayyab ◽  
I. R. Karaş
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Land Use Land Cover ◽  
Limited Memory ◽  
Performance Accuracy ◽  
Satellite Image Classification ◽  
Classification Tool

Abstract. Remote sensing satellite images plays a significant role in mapping land use/land cover LULC. Machine learning ML provide robust functions for satellite image classification. The objective of this paper is to extend the capability of GIS specialists in geospatial area with minimum knowledge in computer science to easily perform ML satellite image classification. A framework consisting 7 stages established. Tools of steps developed in two programing environments, which are ArcGIS for geospatial datasets structuring and Anaconda for ML training and classification. During the development, authors constrained to reduce the complexity of big data of satellite images and limited memory of computers to make tools available for implementation in PC. In addition, automation and improving the performance accuracy. TensorFlow-Keras library employed to perform the classification using neural networks. A case study using RASAT satellite image in Ankara-Turkey utilized to perform the analysis. The developed classifier gained 80% performance accuracy. The complete RASAT satellite image processed and smoothly classified based on blocks methods. The developed tools successfully tested and applied in geospatial area and can be effectively execute in PC by GIS specialist.

Performance of different machine learning algorithms on satellite image classification in rural and urban setup

2020 ◽  
Vol 20 ◽  
pp. 100410
Author(s):  
Ashikur Rahman ◽  
Hasan Muhammad Abdullah ◽  
Md Tousif Tanzir ◽  
Md Jakir Hossain ◽  
Bhoktear M. Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning Algorithms ◽  
Satellite Image ◽  
Machine Learning Algorithms ◽  
Satellite Image Classification ◽  
Rural And Urban

scholarly journals A new approach to the application of conflict redistribution rule in Satellite Image Classification

2020 ◽  
pp. 12-16
Author(s):  
Sofiia Alpert
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Classification Methods ◽  
New Approach ◽  
Remote Sensing Classification ◽  
Satellite Image Classification ◽  
Bodies Of Evidence

Nowadays solution of different scientific problems using satellite images, generally includes a classification procedure. Classification is one of the most important procedures used in remote sensing, because it involves a lot of mathematical operations and data preprocessing. The processing of information and combining of conflicting data is a very difficult problem in classification tasks. Nowadays many classification methods are applied in remote sensing. Classification of conflicting data has been a key problem, both from a theoretical and practical point of view. But a lot of known classification methods can not deal with highly conflicted data and uncertainty. The main purpose of this article is to apply proportional conflict redistribution rule (PRC5) for satellite image classification in conditions of uncertainty, when conflicting sources of evidence give incomplete and vague information. This rule can process conflicting data and combine conflicting bodies of evidence (spectral bands). Proportional conflict redistribution rule can redistribute the partial conflicting mass proportionally on non-empty sets involved in the conflict. It was noticed, that this rule can provide a construction of aggregated estimate under conflict. It calculates all partial conflicting masses separately. It was also shown, that proportional conflict redistribution rule is the most mathematically exact redistribution of conflicting mass to non-empty set. But this rule consists of difficult calculation procedures. The more hypotheses and more masses are involved in the fusion, the more difficult is to implement proportional conflict redistribution rule, therefore special computer software should be used. It was considered an example of practical use of the proposed conflict redistribution rule. It also was noticed, that this new approach to the application of conflict redistribution rule in satellite image classification can be applied for analysis of satellite images, solving practical and ecological tasks, assessment of agricultural lands, classification of forests, in searching for oil and gas.

scholarly journals Object-Based and Rule-Based Classification of Synthetic Aperture Radar Images

2019 ◽  
Vol 8 (10) ◽  
pp. 2458-2463
Keyword(s):  
Image Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Accurate Information ◽  
Barren Land ◽  
Radar Images ◽  
Satellite Image Classification ◽  
Object Based ◽  
Terrain Features

Since thousands of years, the land is the basic and very important requirement for humans to survive and grow. The surface area of the earth provided by nature contains many different geographical locations divided into oceans, mountains, rivers, barren land, fertile land, ice caps and many more. The huge land masses and water bodies need to be observed and analyzed for optimum utilization of resources. Remote sensing is the best possible way to observe the earth's surface from a distance through different satellites and sensors. But most of the satellite images are not clear up to the extent to classify different terrain features accurately. Hence classification of image is needed to observe different terrain features in original images. In this study, the aim is to propose a branch of natural computation for SAR image classification into different terrain features with better information retrieval and accuracy measures as compared to traditional methods for satellite image classification. The object-based analysis has been used to extract spectral reflectance of five texture measures namely urban, rocky, vegetation, water and barren to generate training set. Minimum distance to mean classifier has been used with one of the Nature Inspired computation technique i.e. bacterial foraging optimization algorithm for the satellite image classification, to extract the more accurate information about land area of Alwar district, Rajasthan, India. In the proposed study a high-quality thematic map has been generated with the 7-band multi-spectral, medium-resolution satellite images. This approach provides the greater speed and accuracy in its computation with 97.43% overall accuracy (OA) and 0.96 Kappa co-efficient.

scholarly journals A review of remotely sensed satellite image classification

2019 ◽  
Vol 9 (3) ◽  
pp. 1720 ◽  
Author(s):  
Sakshi Dhingra ◽  
Dharminder Kumar
Keyword(s):  
Image Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Vital Role ◽  
Classification Algorithms ◽  
The Novel ◽  
Extraction Techniques ◽  
Satellite Image Classification ◽  
Vegetation Water ◽  
Processing Steps

<p>Satellite image classification has a vital role for the extraction and analysis of the useful satellite image information. This paper comprises the study of the satellite images classification and Remote Sensing along with a brief overview of the previous studies that are proposed in this field. In this paper, the existing work has been explained utilizing the classification techniques on satellite images of Alwar region in India that covers decent land cover features like Vegetation, Water, Urban, Barren, and Rocky regions. The post- implementation of the classification algorithms, the classified image is obtained displaying different classes that are represented by different colours. Each feature is represented by a different colour and can be easily perceived from the image obtained after classification. The focus of this study is on enhancing the classification accuracy by using proper classifiers along with the novel feature extraction techniques and pre-processing steps. Work of different authors is being discussed in a tabular form defining the methods and outcomes of the respective studies.</p>

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