Automatic building extraction from LiDAR data fusion of point and grid-based features

Du, Shihong; Zhang, Yunsheng; Zou, Zhengrong; Xu, Shengyuan; Xue, He; Chen, Siyang

doi:10.1016/j.isprsjprs.2017.06.005

Cited by 89 publications

(85 citation statements)

References 37 publications

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“…This is an indicator that morphological filters show a worse performance in sloping terrain. In comparison with the lidar data-fusion method (Du et al, 2017), the proposed method shows a lower average area-based quality by 1% but a higher average correctness by 1Á36% in the three areas.…”

Section: Comparison Of Building Detection Performancementioning

confidence: 87%

Building detection and regularisation using DSM and imagery information

Mousa

Helmholz

Belton

et al. 2019

The Photogrammetric Record

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An automatic method for the regularisation of building outlines is presented, utilising a combination of data‐ and model‐driven approaches to provide a robust solution. The core part of the method includes a novel data‐driven approach to generate approximate building polygons from a list of given boundary points. The algorithm iteratively calculates and stores likelihood values between an arbitrary starting boundary point and each of the following boundary points using a function derived from the geometrical properties of a building. As a preprocessing step, building segments have to be identified using a robust algorithm for the extraction of a digital elevation model. Evaluation results on a challenging dataset achieved an average correctness of 96·3% and 95·7% for building detection and regularisation, respectively.

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Section: Comparison Of Building Detection Performancementioning

confidence: 87%

Building detection and regularisation using DSM and imagery information

Mousa

Helmholz

Belton

et al. 2019

The Photogrammetric Record

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“…The first category (unsupervised methods) is mainly developed with some weak prior knowledge or basic assumptions. For example, Du et al introduced a graph cuts-based method and applied it in light detection and ranging data (LiDAR) [1]. Its energy function is constructed by some DLGs update frequently in cities, the noise should maintain at a certain level.…”

Section: Introductionmentioning

confidence: 99%

“…It is common for people to design features in accordance to some prior knowledge from simple observation or basic assumptions. For example, some kinds of hand-crafted features are designed (e.g., contextual features, spatial features, and spectral features) [1,[13][14][15]. As mentioned above, hand-crafted features designed by humans have some limitations.…”

mentioning

confidence: 99%

Extracting Building Areas from Photogrammetric DSM and DOM by Automatically Selecting Training Samples from Historical DLG Data

Chen

Zhang

Nie

et al. 2020

IJGI

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This paper presents an automatic building extraction method which utilizes a photogrammetric digital surface model (DSM) and digital orthophoto map (DOM) with the help of historical digital line graphic (DLG) data. To reduce the need for manual labeling, the initial labels were automatically obtained from historical DLGs. Nonetheless, a proportion of these labels are incorrect due to changes (e.g., new constructions, demolished buildings). To select clean samples, an iterative method using random forest (RF) classifier was proposed in order to remove some possible incorrect labels. To get effective features, deep features extracted from normalized DSM (nDSM) and DOM using the pre-trained fully convolutional networks (FCN) were combined. To control the computation cost and alleviate the burden of redundancy, the principal component analysis (PCA) algorithm was applied to reduce the feature dimensions. Three data sets in two areas were employed with evaluation in two aspects. In these data sets, three DLGs with 15%, 65%, and 25% of noise were applied. The results demonstrate the proposed method could effectively select clean samples, and maintain acceptable quality of extracted results in both pixel-based and object-based evaluations.

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“…As time goes by, our living areas tend to grow bigger and bigger. Without good land use management, we may eventually cause complete deforestation as a result of building constructions for human beings [10,11]. To tackle this problem, we can use satellite images to create time-series data in order to observe how the city has changed-specifically, where new buildings have been built.…”

Section: Introductionmentioning

confidence: 99%

CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection

et al. 2020

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The limitations in obtaining sufficient datasets for training deep learning networks is preventing many applications from achieving accurate results, especially when detecting new constructions using time-series satellite imagery, since this requires at least two images of the same scene and it must contain new constructions in it. To tackle this problem, we introduce Chronological Order Reverse Network (CORN)-an architecture for detecting newly built constructions in time-series SAR images that does not require a large quantity of training data. The network uses two U-net adaptations to learn the changes between images from both Time 1-Time 2 and Time 2-Time 1 formats, which allows it to learn double the amount of changes in different perspectives. We trained the network with 2028 pairs of 256 × 256 pixel SAR images from ALOS-PALSAR, totaling 4056 pairs for the network to learn from, since it learns from both Time 1-Time 2 and Time 2-Time 1. As a result, the network can detect new constructions more accurately, especially at the building boundary, compared to the original U-net trained by the same amount of training data. The experiment also shows that the model trained with CORN can be used with images from Sentinel-1. The source code is available at https://github.com/Raveerat-titech/CORN. Remote Sens. 2020, 12, 990 2 of 25 Remote Sens. 2020, 12, 990 3 of 25 the same ground truth data. While normally, the detection of new buildings is supposed to use the data in Time 1-Time 2 format, our proposed architecture takes both Time 1-Time 2 and Time 2-Time 1 formats of data to allow learning based on both of the changing features to make it more viable. With this architecture, the amount of training data of the network appears to be doubled. This allows the network to be trained with a greater variation of data, and can result an increased detection accuracy without having to use more SAR data or create any additional ground truths. Moreover, CORN has the potential to use SAR images from other satellites and other environments because the training back and forth causes the model to be more robust.In summary, the objective of this paper is to cope with the lack of training data when training deep learning networks, which leads to inaccurate results in newly built construction detection. We do so by proposing a network architecture called "CORN", which doubles the training set by reversing the chronological order of the dataset. CORN contains two U-net adaptations; one trains on Time 1-Time 2 images, and the other one trains on Time 2-Time 1 images. The proposed network not only increases the detection accuracy, but can also be used in a greater variety of settings of the data; specifically, images from other satellites and other acquisition conditions, including the terrain of the testing area.

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Automatic building extraction from LiDAR data fusion of point and grid-based features

Cited by 89 publications

References 37 publications

Building detection and regularisation using DSM and imagery information

Building detection and regularisation using DSM and imagery information

Extracting Building Areas from Photogrammetric DSM and DOM by Automatically Selecting Training Samples from Historical DLG Data

CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection

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