Assessment of point cloud analysis in improving object-based agricultural land cover classification using discrete lidar data in Cabadbaran, Agusan del Norte, Phillippines

Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.

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Vehicle Detection Method Based on Object-Based Point Cloud Analysis Using Vertical Elevation Data

KIPS Transactions on Software and Data Engineering ◽

10.3745/ktsde.2016.5.8.369 ◽

2016 ◽

Vol 5 (8) ◽

pp. 369-376 ◽

Cited By ~ 1

Author(s):

Junbeom Jeon ◽

Heezin Lee ◽

Sangyoon Oh ◽

Minsu Lee

Keyword(s):

Point Cloud ◽

Detection Method ◽

Vehicle Detection ◽

Object Based ◽

Elevation Data ◽

Vertical Elevation ◽

Point Cloud Analysis ◽

Cloud Analysis

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OBJECT BASED AGRICULTURAL LAND COVER CLASSIFICATION MAP OF SHADOWED AREAS FROM AERIAL IMAGE AND LIDAR DATA USING SUPPORT VECTOR MACHINE

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-7-45-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 45-50

Author(s):

R. T. Alberto ◽

S. C. Serrano ◽

G. B. Damian ◽

E. E. Camaso ◽

A. B. Celestino ◽

...

Keyword(s):

Land Cover ◽

Agricultural Land ◽

Texture Classification ◽

Land Cover Classification ◽

Aerial Image ◽

Support Vector ◽

Lidar Data ◽

Segmentation Approach ◽

Great Possibility ◽

Area Classification

Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.

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