Abstract
Abstract. Automatic urban objects extraction from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Lidar data available today. The aim of this study is to propose a new approach for the integration of high-resolution aerial imagery and Lidar data to improve the accuracy of classification in the city complications. In the proposed method, first, the classification of each data is separately performed using Support Vector Machine algorithm. In this case, extracted Normalized Digital Surface Model (nDSM) and pulse intensity are used in classification of LiDAR data, and three spectral visible bands (Red, Green, Blue) are considered as feature vector for the orthoimage classification. Moreover, combining the extracted features of the image and Lidar data another classification is also performed using all the features. The outputs of these classifications are integrated in a decision level fusion system according to the their confusion matrices to find the final classification result. The proposed method was evaluated using an urban area of Zeebruges, Belgium. The obtained results represented several advantages of image fusion with respect to a single shot dataset. With the capabilities of the proposed decision level fusion method, most of the object extraction difficulties and uncertainty were decreased and, the overall accuracy and the kappa values were improved 7% and 10%, respectively.
Highlights
The performance of land cover classification using LiDAR data and Aerial imagery data separately has previously been analyzed and it was shown that superior results were achieved using LiDAR data (Jakubowski et al, 2013)
The results showed enhancement in performance and improvement in the accuracy of the reconstruction and building recognition process(Gilani, Awrangjeb et al 2015).Rastiveis Presented a Decision level fusion of lidar data and aerial color imagery based on Bayesian theory for urban area classification
Classification based on Support Vector Machine (SVM) is applied on orthophoto and Lidar data
Summary
The performance of land cover classification using LiDAR data and Aerial imagery data separately has previously been analyzed and it was shown that superior results were achieved using LiDAR data (Jakubowski et al, 2013). Multiple sensors may provide complementary data, and fusion of information of different sensors can produce a better understanding of the observed site, which is not possible with single sensor (Simone, Farina et al 2002). Signals from multiple sensors are combined together to create a new signal with a better signal-tonoise ratio than the input signals. The information from different images, pixel by pixel, are merged to improve detection of objects in some tasks such as segmentation. Feature level fusion consists of merging the features extracted from different images. In this level of fusion, features are extracted from different sensors and combined to create a feature vector for classified using a classifier method (Abbasi, Arefi et al 2015)
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