Conjunction and synchronization methods of earth satellite images with local cartographic data

Dymkova, Svetlana

doi:10.1109/ieeeconf48371.2020.9078561

Cited by 23 publications

(3 citation statements)

References 7 publications

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“…To interpret these images, research on image spatial decomposition analysis is increasing. Many images are overlapped to convert satellite images into 3D [4][5][6]. Since digital surface model (DSM) images [7][8][9][10], which contain so much information, are generally lower in resolution than LiDAR data [11][12][13], research on 3D pointer cloud Easy Matching technology continues to improve with a focus on accuracy [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

CNN Algorithm for Roof Detection and Material Classification in Satellite Images

et al. 2021

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This paper suggests an algorithm for extracting the location of a building from satellite imagery and using that information to modify the roof content. The materials are determined by measuring the conditions where the building is located and detecting the position of a building in broad satellite images. Depending on the incomplete roof or material, there is a greater possibility of great damage caused by disaster situations or external shocks. To address these problems, we propose an algorithm to detect roofs and classify materials in satellite images. Satellite imaging locates areas where buildings are likely to exist based on roads. Using images of the detected buildings, we classify the material of the roof using a proposed convolutional neural network (CNN) model algorithm consisting of 43 layers. In this paper, we propose a CNN structure to detect areas with buildings in large images and classify roof materials in the detected areas.

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Section: Introductionmentioning

confidence: 99%

CNN Algorithm for Roof Detection and Material Classification in Satellite Images

et al. 2021

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“…The data retrieved from GIS are in the form of cartographic, photographic, or tabulated values stored in a spreadsheets format. Cartographic data defined as the data which is stated in map-form including geographical information and survey-data results, while photographic data defined as visualaided data [25]. Besides these data, other digital data also had been contributed to the GIS.…”

Section: Gis-based Datamentioning

confidence: 99%

Gis-Aided Geographical and Meteorological Data Overview of Solar Radiation Mapping for Malaysia – An Explanatory Study Based on Solar Radiation Prediction Modeling Using Neural Network Approach

2021

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Solar radiation mapping has used geographical and meteorological data. To obtain geographical and meteorological data, a Geographic Information System (GIS) is required. GIS is defined as an integrated geographic resource that presents data in terms of spatial information. This data is important for Neural Networks as it will be used as input parameters for the development of solar radiation prediction models. Solar radiation prediction is one way to map the sun's rays in certain places where there are insufficient resources or space to build a complete solar radiation measurement station. Since predictions about solar radiation require meteorological and geographical data, this paper will give an overview of GIS-assisted geographical and meteorological data to be used as input parameters for solar radiation mapping which will eventually be used as input for prediction models developed for the whole country of Malaysia using Neural Networks. Based on the results, the prediction model developed managed to obtain a coefficient of determination, R2 value of 0.9329.

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“…Consequently, the image processing field has various applications like military, medical, etc [12]. Nowadays, images are everywhere, and generating images in huge amounts is an easy task for everyone [13]. With such images profusion, traditional techniques for image processing have undergoes more complex issues and have to face the human vision based on their adaptability [14].…”

Section: Introductionmentioning

confidence: 99%

A Review on Neural Approaches in Image Processing Applications

Bhalla¹

2023

IJRASET

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Image processing is the manipulation and analysis of a digitalized image; it especially improves the image quality. Also, it yields indispensable facts about the image processing techniques required for image enhancement, restoration, preprocessing, and segmentation. These methods help to provide earlier object detection and prevent further impacts due to segmentation and classification. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on Machine Learning (ML) in image processing review has revealed distinct methodologies with diverse frameworks utilized for object detection. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, ML approaches were compared and reported as the best model. Moreover, different kinds of datasets were used to detect the objects and unknown users or intruders. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, BRATS, and ImageNet. This article discloses the implementation capacity of distinct techniques implemented for each processing methods like supervised and unsupervised. Finally, the Naïve Bayes and LMS model achieved 100% accuracy as finest. Moreover, this technique has utilized public datasets to verify the efficiency. Hence, the overall review of this article has revealed a method for detecting images effectively.

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Conjunction and synchronization methods of earth satellite images with local cartographic data

Cited by 23 publications

References 7 publications

CNN Algorithm for Roof Detection and Material Classification in Satellite Images

CNN Algorithm for Roof Detection and Material Classification in Satellite Images

Gis-Aided Geographical and Meteorological Data Overview of Solar Radiation Mapping for Malaysia – An Explanatory Study Based on Solar Radiation Prediction Modeling Using Neural Network Approach

A Review on Neural Approaches in Image Processing Applications

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