Building Change Detection Using Old Aerial Images and New LiDAR Data

Du, Shihong; Zhang, Yunsheng; Qin, Rongjun; Yang, Zhihua; Zou, Zhengrong; Tang, Yuqi; Fan, Chong

doi:10.3390/rs8121030

Cited by 59 publications

(43 citation statements)

References 48 publications

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“…Huang et al [22] proposed a morphological building index (MBI), to build a relationship between the spectral-spatial characteristics of buildings and morphological operators [22]. Du et al [48] detected building changes in urban areas using aerial images and LiDAR data. Liu et al [49] used a line-constrained shape feature to capture the shape characteristics of a building.…”

Section: Introductionmentioning

confidence: 99%

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

et al. 2019

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We present a novel convolutional neural network (CNN)-based change detection framework for locating changed building instances as well as changed building pixels from very high resolution (VHR) aerial images. The distinctive advantage of the framework is the self-training ability, which is highly important in deep-learning-based change detection in practice, as high-quality samples of changes are always lacking for training a successful deep learning model. The framework consists two parts: a building extraction network to produce a binary building map and a building change detection network to produce a building change map. The building extraction network is implemented with two widely used structures: a Mask R-CNN for object-based instance segmentation, and a multi-scale full convolutional network for pixel-based semantic segmentation. The building change detection network takes bi-temporal building maps produced from the building extraction network as input and outputs a building change map at the object and pixel levels. By simulating arbitrary building changes and various building parallaxes in the binary building map, the building change detection network is well trained without real-life samples. This greatly lowers the requirements of labeled changed buildings, and guarantees the algorithm's robustness to registration errors caused by parallaxes. To evaluate the proposed method, we chose a wide range of urban areas from an open-source dataset as training and testing areas, and both pixel-based and object-based model evaluation measures were used. Experiments demonstrated our approach was vastly superior: without using any real change samples, it reached 63% average precision (AP) at the object (building instance) level. In contrast, with adequate training samples, other methods-including the most recent CNN-based and generative adversarial network (GAN)-based ones-have only reached 25% AP in their best cases.

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Section: Introductionmentioning

confidence: 99%

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

et al. 2019

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“…The combination of image-based dense digital surface model reconstruction from historical aerial imagery with object-based image analysis was used for the detection of individual buildings and the subsequent analysis of settlement change (Nebiker et al, 2014). The other study introduced building change detection comparing old dense point cloud and new LIDAR images (Du et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Urban Area Change Detection Using Time Series Aerial Images

Altuntas

2018

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

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ABSTRACT:The urban area should be imaged in three-dimensional (3D) for planning, inspection and management. In addition fast urbanisation requires detection the urban area changes which have been occurred with new buildings, additional floor to current buildings and excavations. 3D surface model of urban area enables to extracting high information from them. On the other hand high density spatial data should be measured to creating 3D digital terrain surface model. The dense image matching method makes 3D measurement with high density from the images in a short time. The aim of this study is detection the urban area changes via comparison of time series point cloud data from historical stereoscopic aerial images. The changes were detected with the difference of these digital elevation models. The study area was selected from Konya city in Turkey, and it has a large number of new buildings and changes in topography. Dense point cloud data were created from historical aerial images belong to years of 1951, 1975 and 2010. Every threedimensional point cloud data were registered to global georeferenced coordinate system with ground control points created from the imaged objects such as building corner, fence, wall and etc. Then urban changes were detected with comparing the dense point cloud data by exploiting the iterative closest point (ICP) algorithm. Consequently, the urban changes were detected from point to surface distances between image based point clouds.

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“…The proposed framework performs better than state-of-the-art building extraction methods, given its higher values of recall, precision, and intersection over Union (IoU).2 of 33 remote sensing, and is extensively used in various applications, including urban planning, cartographic mapping, and land use analysis [1,2]. The significant progress in sensors and operating platforms has enabled us to acquire remote sensing images and 3D point clouds from cameras or Light Detection And Ranging (LiDAR) equipped in various platforms (e.g., satellite, aerial, and Unmanned Aerial Vehicle (UAV) platforms); thus, the methods based on images and point clouds are commonly used to extract buildings [3][4][5].Building extraction can be broadly divided into three categories according to data source: 2D image-based methods, 3D point cloud-based methods, and 2D and 3D information hybrid methods. 2D image-based building extraction consists of two stages, namely, building segmentation and regularization.…”

mentioning

confidence: 99%

“…2 of 33 remote sensing, and is extensively used in various applications, including urban planning, cartographic mapping, and land use analysis [1,2]. The significant progress in sensors and operating platforms has enabled us to acquire remote sensing images and 3D point clouds from cameras or Light Detection And Ranging (LiDAR) equipped in various platforms (e.g., satellite, aerial, and Unmanned Aerial Vehicle (UAV) platforms); thus, the methods based on images and point clouds are commonly used to extract buildings [3][4][5].…”

mentioning

confidence: 99%

Building Extraction from UAV Images Jointly Using 6D-SLIC and Multiscale Siamese Convolutional Networks

Zhou

Chen

et al. 2019

Remote Sensing

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Automatic building extraction using a single data type, either 2D remotely-sensed images or light detection and ranging 3D point clouds, remains insufficient to accurately delineate building outlines for automatic mapping, despite active research in this area and the significant progress which has been achieved in the past decade. This paper presents an effective approach to extracting buildings from Unmanned Aerial Vehicle (UAV) images through the incorporation of superpixel segmentation and semantic recognition. A framework for building extraction is constructed by jointly using an improved Simple Linear Iterative Clustering (SLIC) algorithm and Multiscale Siamese Convolutional Networks (MSCNs). The SLIC algorithm, improved by additionally imposing a digital surface model for superpixel segmentation, namely 6D-SLIC, is suited for building boundary detection under building and image backgrounds with similar radiometric signatures. The proposed MSCNs, including a feature learning network and a binary decision network, are used to automatically learn a multiscale hierarchical feature representation and detect building objects under various complex backgrounds. In addition, a gamma-transform green leaf index is proposed to truncate vegetation superpixels for further processing to improve the robustness and efficiency of building detection, the Douglas–Peucker algorithm and iterative optimization are used to eliminate jagged details generated from small structures as a result of superpixel segmentation. In the experiments, the UAV datasets, including many buildings in urban and rural areas with irregular shapes and different heights and that are obscured by trees, are collected to evaluate the proposed method. The experimental results based on the qualitative and quantitative measures confirm the effectiveness and high accuracy of the proposed framework relative to the digitized results. The proposed framework performs better than state-of-the-art building extraction methods, given its higher values of recall, precision, and intersection over Union (IoU).

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Building Change Detection Using Old Aerial Images and New LiDAR Data

Cited by 59 publications

References 48 publications

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

Urban Area Change Detection Using Time Series Aerial Images

Building Extraction from UAV Images Jointly Using 6D-SLIC and Multiscale Siamese Convolutional Networks

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