Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas
Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data....
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2022
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ftdoajarticles:oai:doaj.org/article:a151b4a04a234579920fc95e95d016fe 2023-05-15T16:22:14+02:00 Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas Shridhar D. Jawak Sagar F. Wankhede Alvarinho J. Luis Keshava Balakrishna 2022-09-01T00:00:00Z https://doi.org/10.3390/rs14174403 https://doaj.org/article/a151b4a04a234579920fc95e95d016fe EN eng MDPI AG https://www.mdpi.com/2072-4292/14/17/4403 https://doaj.org/toc/2072-4292 doi:10.3390/rs14174403 2072-4292 https://doaj.org/article/a151b4a04a234579920fc95e95d016fe Remote Sensing, Vol 14, Iss 4403, p 4403 (2022) geographic object-based image analysis atmospheric correction pansharpening WorldView-2 Ny-Ålesund Chandra–Bhaga basin Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14174403 2022-12-30T21:08:46Z Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of GEOBIA for glacier surface mapping, however, necessitates multiple scales of segmentation and input of supportive ancillary data. The mapping of glacier surface facies presents a unique problem to GEOBIA on account of its separable but closely matching spectral characteristics and often disheveled surface. Debris cover can induce challenges and requires additions of slope, temperature, and short-wave infrared data as supplements to enable efficient mapping. Moreover, as the influence of atmospheric corrections and image sharpening can derive variations in the apparent surface reflectance, a robust analysis of the effects of these processing routines in a GEOBIA environment is lacking. The current study aims to investigate the impact of three atmospheric corrections, Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods, viz., Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on the classification of surface facies using GEOBIA. This analysis is performed on VHR WorldView-2 imagery of selected glaciers in Ny-Ålesund, Svalbard, and Chandra–Bhaga basin, Himalaya. The image subsets are segmented using multiresolution segmentation with constant parameters. Three rule sets are defined: rule set 1 utilizes only spectral information, rule set 2 contains only spatial and contextual features, and rule set 3 combines both spatial and spectral attributes. Rule set 3 performs the best across all processing schemes with the highest overall accuracy, followed by rule set 1 and lastly rule set 2. This trend is observed for every image subset. Among the atmospheric corrections, DOS displays ... Article in Journal/Newspaper glacier Ny Ålesund Ny-Ålesund Svalbard Directory of Open Access Journals: DOAJ Articles Svalbard Ny-Ålesund Remote Sensing 14 17 4403 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
geographic object-based image analysis atmospheric correction pansharpening WorldView-2 Ny-Ålesund Chandra–Bhaga basin Science Q |
spellingShingle |
geographic object-based image analysis atmospheric correction pansharpening WorldView-2 Ny-Ålesund Chandra–Bhaga basin Science Q Shridhar D. Jawak Sagar F. Wankhede Alvarinho J. Luis Keshava Balakrishna Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas |
topic_facet |
geographic object-based image analysis atmospheric correction pansharpening WorldView-2 Ny-Ålesund Chandra–Bhaga basin Science Q |
description |
Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of GEOBIA for glacier surface mapping, however, necessitates multiple scales of segmentation and input of supportive ancillary data. The mapping of glacier surface facies presents a unique problem to GEOBIA on account of its separable but closely matching spectral characteristics and often disheveled surface. Debris cover can induce challenges and requires additions of slope, temperature, and short-wave infrared data as supplements to enable efficient mapping. Moreover, as the influence of atmospheric corrections and image sharpening can derive variations in the apparent surface reflectance, a robust analysis of the effects of these processing routines in a GEOBIA environment is lacking. The current study aims to investigate the impact of three atmospheric corrections, Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods, viz., Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on the classification of surface facies using GEOBIA. This analysis is performed on VHR WorldView-2 imagery of selected glaciers in Ny-Ålesund, Svalbard, and Chandra–Bhaga basin, Himalaya. The image subsets are segmented using multiresolution segmentation with constant parameters. Three rule sets are defined: rule set 1 utilizes only spectral information, rule set 2 contains only spatial and contextual features, and rule set 3 combines both spatial and spectral attributes. Rule set 3 performs the best across all processing schemes with the highest overall accuracy, followed by rule set 1 and lastly rule set 2. This trend is observed for every image subset. Among the atmospheric corrections, DOS displays ... |
format |
Article in Journal/Newspaper |
author |
Shridhar D. Jawak Sagar F. Wankhede Alvarinho J. Luis Keshava Balakrishna |
author_facet |
Shridhar D. Jawak Sagar F. Wankhede Alvarinho J. Luis Keshava Balakrishna |
author_sort |
Shridhar D. Jawak |
title |
Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas |
title_short |
Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas |
title_full |
Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas |
title_fullStr |
Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas |
title_full_unstemmed |
Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas |
title_sort |
effect of image-processing routines on geographic object-based image analysis for mapping glacier surface facies from svalbard and the himalayas |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14174403 https://doaj.org/article/a151b4a04a234579920fc95e95d016fe |
geographic |
Svalbard Ny-Ålesund |
geographic_facet |
Svalbard Ny-Ålesund |
genre |
glacier Ny Ålesund Ny-Ålesund Svalbard |
genre_facet |
glacier Ny Ålesund Ny-Ålesund Svalbard |
op_source |
Remote Sensing, Vol 14, Iss 4403, p 4403 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/17/4403 https://doaj.org/toc/2072-4292 doi:10.3390/rs14174403 2072-4292 https://doaj.org/article/a151b4a04a234579920fc95e95d016fe |
op_doi |
https://doi.org/10.3390/rs14174403 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
17 |
container_start_page |
4403 |
_version_ |
1766010199802380288 |