Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood
Limitations and deficiencies of different remote sensing sensors in extraction of different objects caused fusion of data from different sensors to become more widespread for improving classification results. Using a variety of data which are provided from different sensors, increase the spatial and...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2015-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/569/2015/isprsarchives-XL-7-W3-569-2015.pdf |
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| author | B. Abbasi H. Arefi B. Bigdeli M. Motagh S. Roessner |
| author_facet | B. Abbasi H. Arefi B. Bigdeli M. Motagh S. Roessner |
| author_sort | B. Abbasi |
| collection | DOAJ |
| description | Limitations and deficiencies of different remote sensing sensors in extraction of different objects caused fusion of data from different
sensors to become more widespread for improving classification results. Using a variety of data which are provided from different
sensors, increase the spatial and the spectral accuracy. Lidar (Light Detection and Ranging) data fused together with hyperspectral
images (HSI) provide rich data for classification of the surface objects. Lidar data representing high quality geometric information
plays a key role for segmentation and classification of elevated features such as buildings and trees. On the other hand, hyperspectral
data containing high spectral resolution would support high distinction between the objects having different spectral information
such as soil, water, and grass. This paper presents a fusion methodology on Lidar and hyperspectral data for improving classification
accuracy in urban areas. In first step, we applied feature extraction strategies on each data separately. In this step, texture features
based on GLCM (Grey Level Co-occurrence Matrix) from Lidar data and PCA (Principal Component Analysis) and MNF
(Minimum Noise Fraction) based dimension reduction methods for HSI are generated. In second step, a Maximum Likelihood (ML)
based classification method is applied on each feature spaces. Finally, a fusion method is applied to fuse the results of classification.
A co-registered hyperspectral and Lidar data from University of Houston was utilized to examine the result of the proposed method.
This data contains nine classes: Building, Tree, Grass, Soil, Water, Road, Parking, Tennis Court and Running Track. Experimental
investigation proves the improvement of classification accuracy to 88%. |
| first_indexed | 2024-12-22T21:08:32Z |
| format | Article |
| id | doaj.art-6f71d7311139454ea419b22fc3707950 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-22T21:08:32Z |
| publishDate | 2015-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-6f71d7311139454ea419b22fc37079502022-12-21T18:12:36ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342015-04-01XL-7/W356957310.5194/isprsarchives-XL-7-W3-569-2015Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihoodB. Abbasi0H. Arefi1B. Bigdeli2M. Motagh3S. Roessner4Department of Geomatics and Surveying Eng., University of Tehran, Tehran, IranDepartment of Geomatics and Surveying Eng., University of Tehran, Tehran, IranDepartment of Geomatics and Surveying Eng., University of Tehran, Tehran, IranDepartment of Geomatics and Surveying Eng., University of Tehran, Tehran, IranGFZ German Research Centre for Geosciences, Section of Remote Sensing, 14473, Potsdam, GermanyLimitations and deficiencies of different remote sensing sensors in extraction of different objects caused fusion of data from different sensors to become more widespread for improving classification results. Using a variety of data which are provided from different sensors, increase the spatial and the spectral accuracy. Lidar (Light Detection and Ranging) data fused together with hyperspectral images (HSI) provide rich data for classification of the surface objects. Lidar data representing high quality geometric information plays a key role for segmentation and classification of elevated features such as buildings and trees. On the other hand, hyperspectral data containing high spectral resolution would support high distinction between the objects having different spectral information such as soil, water, and grass. This paper presents a fusion methodology on Lidar and hyperspectral data for improving classification accuracy in urban areas. In first step, we applied feature extraction strategies on each data separately. In this step, texture features based on GLCM (Grey Level Co-occurrence Matrix) from Lidar data and PCA (Principal Component Analysis) and MNF (Minimum Noise Fraction) based dimension reduction methods for HSI are generated. In second step, a Maximum Likelihood (ML) based classification method is applied on each feature spaces. Finally, a fusion method is applied to fuse the results of classification. A co-registered hyperspectral and Lidar data from University of Houston was utilized to examine the result of the proposed method. This data contains nine classes: Building, Tree, Grass, Soil, Water, Road, Parking, Tennis Court and Running Track. Experimental investigation proves the improvement of classification accuracy to 88%.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/569/2015/isprsarchives-XL-7-W3-569-2015.pdf |
| spellingShingle | B. Abbasi H. Arefi B. Bigdeli M. Motagh S. Roessner Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood |
| title_full | Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood |
| title_fullStr | Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood |
| title_full_unstemmed | Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood |
| title_short | Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood |
| title_sort | fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood |
| url | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/569/2015/isprsarchives-XL-7-W3-569-2015.pdf |
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