Improving the Classification of LANDSAT-8 OLI Images Using Neighborhood Median Pixel Values

Image classification in remote sensing is defined by categorizing image pixels or raw data sensed by satellites into a distinct set of labels. In this paper, an improved technique for classifying vegetation, built-up, and water pixels from satellite images is proposed. The technique makes use of the...

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Main Author: Magpantay, Abraham
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Published: Archīum Ateneo 2020
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Online Access:https://archium.ateneo.edu/theses-dissertations/403
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spelling ph-ateneo-arc.theses-dissertations-15292021-09-27T03:00:04Z Improving the Classification of LANDSAT-8 OLI Images Using Neighborhood Median Pixel Values Magpantay, Abraham Image classification in remote sensing is defined by categorizing image pixels or raw data sensed by satellites into a distinct set of labels. In this paper, an improved technique for classifying vegetation, built-up, and water pixels from satellite images is proposed. The technique makes use of the median value of all the pixels in the rectangular neighborhood centered at the given pixel to be classified. A scoring system was developed that compares this median value in relation to the expected median values for each of the different classes. The proposed method was tested on Landsat-8 Operational Land Imager (OLI) bands 1 to 7 images and three index images— Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The experimental results showed an overall accuracy of 94%, a remarkable improvement from the 84% accuracy of the previous work that uses a distance-based classifier. The obtained results indicate that the proposed method can be a better alternative for classifying images in remote sensing. 2020-01-01T08:00:00Z text https://archium.ateneo.edu/theses-dissertations/403 Theses and Dissertations (All) Archīum Ateneo n/a
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
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Magpantay, Abraham
Improving the Classification of LANDSAT-8 OLI Images Using Neighborhood Median Pixel Values
description Image classification in remote sensing is defined by categorizing image pixels or raw data sensed by satellites into a distinct set of labels. In this paper, an improved technique for classifying vegetation, built-up, and water pixels from satellite images is proposed. The technique makes use of the median value of all the pixels in the rectangular neighborhood centered at the given pixel to be classified. A scoring system was developed that compares this median value in relation to the expected median values for each of the different classes. The proposed method was tested on Landsat-8 Operational Land Imager (OLI) bands 1 to 7 images and three index images— Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The experimental results showed an overall accuracy of 94%, a remarkable improvement from the 84% accuracy of the previous work that uses a distance-based classifier. The obtained results indicate that the proposed method can be a better alternative for classifying images in remote sensing.
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author Magpantay, Abraham
author_facet Magpantay, Abraham
author_sort Magpantay, Abraham
title Improving the Classification of LANDSAT-8 OLI Images Using Neighborhood Median Pixel Values
title_short Improving the Classification of LANDSAT-8 OLI Images Using Neighborhood Median Pixel Values
title_full Improving the Classification of LANDSAT-8 OLI Images Using Neighborhood Median Pixel Values
title_fullStr Improving the Classification of LANDSAT-8 OLI Images Using Neighborhood Median Pixel Values
title_full_unstemmed Improving the Classification of LANDSAT-8 OLI Images Using Neighborhood Median Pixel Values
title_sort improving the classification of landsat-8 oli images using neighborhood median pixel values
publisher Archīum Ateneo
publishDate 2020
url https://archium.ateneo.edu/theses-dissertations/403
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