Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska
Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have conside...
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ftunivnorthcag:oai:libres.uncg.edu/393 2024-02-11T10:00:34+01:00 Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska Chaudhuri, Debasish NC DOCKS at The University of North Carolina at Greensboro 2008 http://libres.uncg.edu/ir/uncg/f/umi-uncg-1673.pdf English eng http://libres.uncg.edu/ir/uncg/f/umi-uncg-1673.pdf Vegetation mapping $z Arctic regions Vegetation mapping $x Remote sensing Neural networks (Computer science) Pattern recognition systems Vegetation surveys Vegetation classification Ecological mapping $x Remote sensing 2008 ftunivnorthcag 2024-01-27T23:43:58Z Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was used as an input to a Multilayer Perceptron (MLP) ... Other/Unknown Material Arctic north slope Tundra Alaska University of North Carolina: NC DOCKS (Digital Online Collection of Knowledge and Scholarship) Arctic |
institution |
Open Polar |
collection |
University of North Carolina: NC DOCKS (Digital Online Collection of Knowledge and Scholarship) |
op_collection_id |
ftunivnorthcag |
language |
English |
topic |
Vegetation mapping $z Arctic regions Vegetation mapping $x Remote sensing Neural networks (Computer science) Pattern recognition systems Vegetation surveys Vegetation classification Ecological mapping $x Remote sensing |
spellingShingle |
Vegetation mapping $z Arctic regions Vegetation mapping $x Remote sensing Neural networks (Computer science) Pattern recognition systems Vegetation surveys Vegetation classification Ecological mapping $x Remote sensing Chaudhuri, Debasish NC DOCKS at The University of North Carolina at Greensboro Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska |
topic_facet |
Vegetation mapping $z Arctic regions Vegetation mapping $x Remote sensing Neural networks (Computer science) Pattern recognition systems Vegetation surveys Vegetation classification Ecological mapping $x Remote sensing |
description |
Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was used as an input to a Multilayer Perceptron (MLP) ... |
author |
Chaudhuri, Debasish NC DOCKS at The University of North Carolina at Greensboro |
author_facet |
Chaudhuri, Debasish NC DOCKS at The University of North Carolina at Greensboro |
author_sort |
Chaudhuri, Debasish |
title |
Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska |
title_short |
Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska |
title_full |
Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska |
title_fullStr |
Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska |
title_full_unstemmed |
Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska |
title_sort |
hybrid image classification technique for land-cover mapping in the arctic tundra, north slope, alaska |
publishDate |
2008 |
url |
http://libres.uncg.edu/ir/uncg/f/umi-uncg-1673.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic north slope Tundra Alaska |
genre_facet |
Arctic north slope Tundra Alaska |
op_relation |
http://libres.uncg.edu/ir/uncg/f/umi-uncg-1673.pdf |
_version_ |
1790596282547437568 |