Segment-based characterization of roof surfaces using hyperspectral and laser scanning data

Lemp, D.; Weidner, Uwe

doi:10.1109/igarss.2005.1526783

Cited by 18 publications

(19 citation statements)

References 2 publications

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“…Due to the increased availability of HS and LiDAR data from the same area, the fusion of these remote sensing data have been of great interest for many practical applications [1,3,4]. Lemp and Weidner [1] exploit HS and LiDAR data for the classification of urban areas, using LiDAR for the segmentation of the scene, and then HS data for the classification of the resulting regions.…”

Section: Introductionmentioning

confidence: 99%

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Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

Liao

Bellens

Pižurica

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

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This paper proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first few principal components (PCs) of original hyperspectral (HS) image. We got the fused features by projecting the spectral (original HS image), spatial and elevation features onto a lower subspace through a graph-based feature fusion method. Then, we got four classification maps by using spectral features, spatial features, elevation features and the graph fused features individually as input of SVM classifier. The final classification map was obtained by fusing the four classification maps through the weighted majority voting. Experimental results on fusion of HS and LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or only feature fusion, with the proposed method, overall classification accuracies were improved by 10% and 2%, respectively.

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Section: Introductionmentioning

confidence: 99%

“…Lemp and Weidner [1] exploit HS and LiDAR data for the classification of urban areas, using LiDAR for the segmentation of the scene, and then HS data for the classification of the resulting regions. The joint use of HS and LiDAR remote sensing data for the classification of complex forest areas was investigated in [3].…”

Section: Introductionmentioning

confidence: 99%

Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

Liao

Bellens

Pižurica

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

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“…La utilización de datos hiperespectrales y LIDAR para la clasificación de áreas urbanas [8] se ha centrado en la segmentación de la escena mediante los datos LIDAR y una clasificación de las regiones resultantes a partir de los datos hiperespectrales.…”

Section: Introductionunclassified

Analysis of thematic classified aerial images trough multispectral and LIDAR data

Arquero

Martínez

2011

IEEE Latin Am. Trans.

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“…As HSI provide a widespread description of the spectral information in some classes without any height information such as building, street and vegetation, fusion of this data with Lidar data may improve classification results in areas with different height. Lemp et al (2005) fused hyperspectral and Lidar data for improving classification of urban areas. They used Lidar data for segmentation and hyperspectral data for classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood

Arand

Arefi

Bigdeli

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: Lidar, Hyper-spectral, Fusion, Classification, Maximum Likelihood ABSTRACT: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%.

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Segment-based characterization of roof surfaces using hyperspectral and laser scanning data

Cited by 18 publications

References 2 publications

Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

Analysis of thematic classified aerial images trough multispectral and LIDAR data

Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood

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