Development of deep learning-based fusion method for building detection using LiDAR and very high resolution images
Buildings play an essential role in urban construction, planning, and climate studies. Extracting detailed and accurate information about building such as value, usage, height, and size provides information for town planning, urban management, and three-dimensional (3D) visualization. Building ex...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/92183/1/FK%202019%2055%20-%20IR.pdf |
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| Summary: | Buildings play an essential role in urban construction, planning, and climate
studies. Extracting detailed and accurate information about building such as
value, usage, height, and size provides information for town planning, urban
management, and three-dimensional (3D) visualization. Building extraction with
remote sensing data especially LiDAR (Light Detection And Ranging) and VHR
(Very High Resolution) images is a difficult task and open research problem. For
this purpose, scientists have been developing methods utilizing the standard pixel
features and additional height features of the data in various ways. In urban areas,
extracting buildings is more complex than extracting them in rural areas. This is
because of the presence of nearby objects, such as trees, which frequently have
similar elevations as buildings. Additional challenges also come from different
material combinations that create a variety of intensity in the spectral bands,
employed. Two common methods are widely used in literature, pixel-based and
object-based methods (also known as OBIA). The former results in salt and
pepper like noise in the detected buildings, while the latter requires proper feature
selection and image segmentation. Both methods have poor generalization and
transferability to other environments, scale dependency, and require good quality
training examples. As a result, the main goal of this research is to design and
optimize deep learning-based fusion techniques using Autoencoders (AE) and
Convolutional Neural Networks (CNN) for integrating LiDAR and Worldview-3
(WV3) data for building extraction. The optimization was carried out using grid
and random search techniques to improve the performance of models.
Specifically, two fusion methods were developed. First, a method for fusion of
LiDAR-based digital surface model (DSM) with orthophoto (LO-Fusion), and a
second method for LiDAR-DSM with WV3 (LW-Fusion) image. The results of this
thesis are promising. Our method achieved the highest accuracies of 97.34%,
94.48%, and 94.37% in the three-subset areas. It performed better than the traditional methods, such as support vector machine (SVM), random forest (RF),
and K-nearest neighbour (KNN). The highest validation accuracy in this group of
methods was 89.04%, achieved by SVM. Although KNN achieved better training
accuracy (92.34%) than RF, the latter achieved better validation accuracy than
the former (86.17%). Furthermore, CNN and Optimized CNN with the random
search were used to detect buildings in the same areas using only LiDAR and
orthophoto data. The experimental results show that the use of additional features
of WV3 image fused with LiDAR data can increase validation accuracy by almost
11%. The validation accuracy of Optimized CNN with only LiDAR and orthophoto
data was 86.19%, which is relatively lower than those of SVM and RF. Overall,
proper optimization can improve the use of deep learning models such as CNN
and autoencoders to the extent of outperforming OBIA for building detection from
LiDAR and VHR satellite data. |
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