scholarly journals Modified Superpixel Segmentation for Digital Surface Model Refinement and Building Extraction from Satellite Stereo Imagery

2018 ◽  
Vol 10 (11) ◽  
pp. 1824 ◽  
Author(s):  
Zeinab Gharibbafghi ◽  
Jiaojiao Tian ◽  
Peter Reinartz
Keyword(s):  
Satellite Images ◽  
Surface Model ◽  
Digital Surface Model ◽  
Building Extraction ◽  
Edge Preserving ◽  
Urban Scenes ◽  
Superpixel Segmentation ◽  
Multi Scale ◽  
Stereo Imagery ◽  
Edge Based

Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High-Resolution (VHR) satellite images especially in complex urban scenes. In this paper, we develop a superpixel algorithm as a modified edge-based version of Simple Linear Iterative Clustering (SLIC), which is here called ESLIC, compatible with VHR satellite images. Then, based on the modified properties of generated superpixels, a heuristic multi-scale approach for building extraction is proposed, based on the stereo satellite imagery along with the corresponding Digital Surface Model (DSM). First, to generate the modified superpixels, an edge-preserving term is applied to retain the main building boundaries and edges. The resulting superpixels are then used to initially refine the stereo-extracted DSM. After shadow and vegetation removal, a rough building mask is obtained from the normalized DSM, which highlights the appropriate regions in the image, to be used as the input of a multi-scale superpixel segmentation of the proper areas to determine the superpixels inside the building. Finally, these building superpixels with different scales are integrated and the output is a unified building mask. We have tested our methods on building samples from a WorldView-2 dataset. The results are promising, and the experiments show that superpixels generated with the proposed ESLIC algorithm are more adherent to the building boundaries, and the resulting building mask retains urban object shape better than those generated with the original SLIC algorithm.

scholarly journals AUTOMATIC BUILDING EXTRACTION AND ROOF RECONSTRUCTION IN 3K IMAGERY BASED ON LINE SEGMENTS

2016 ◽  
Vol XLI-B3 ◽  
pp. 625-631
Author(s):  
A. Köhn ◽  
J. Tian ◽  
F. Kurz
Keyword(s):  
3D Models ◽  
Surface Model ◽  
Building Extraction ◽  
Line Segments ◽  
Roof Structure ◽  
Line Segment Detection ◽  
Fitting Algorithm ◽  
Morphological Filtering ◽  
Stereo Imagery ◽  
On Line

We propose an image processing workflow to extract rectangular building footprints using georeferenced stereo-imagery and a derivative digital surface model (DSM) product. The approach applies a line segment detection procedure to the imagery and subsequently verifies identified line segments individually to create a footprint on the basis of the DSM. The footprint is further optimized by morphological filtering. Towards the realization of 3D models, we decompose the produced footprint and generate a 3D point cloud from DSM height information. By utilizing the robust RANSAC plane fitting algorithm, the roof structure can be correctly reconstructed. In an experimental part, the proposed approach has been performed on 3K aerial imagery.

scholarly journals Boundary-Aware Refined Network for Automatic Building Extraction in Very High-Resolution Urban Aerial Images

2021 ◽  
Vol 13 (4) ◽  
pp. 692
Author(s):  
Yuwei Jin ◽  
Wenbo Xu ◽  
Ce Zhang ◽  
Xin Luo ◽  
Haitao Jia
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Aerial Images ◽  
Data Sets ◽  
Building Extraction ◽  
Visual Interpretation ◽  
Urban Scenes ◽  
Multi Scale ◽  
Spatial Pyramid Pooling ◽  
Very High

Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial images. However, due to the unstable multi-scale context aggregation, the insufficient combination of multi-level features and the lack of consideration of the semantic boundary, most existing CNNs produce incomplete segmentation for large-scale buildings and result in predictions with huge uncertainty at building boundaries. This paper presents a novel network with a special boundary-aware loss embedded, called the Boundary-Aware Refined Network (BARNet), to address the gap above. The unique properties of the proposed BARNet are the gated-attention refined fusion unit, the denser atrous spatial pyramid pooling module, and the boundary-aware loss. The performance of the BARNet is tested on two popular data sets that include various urban scenes and diverse patterns of buildings. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches in both visual interpretation and quantitative evaluations.

scholarly journals EXTRACTION OF BUILDING ROOF EDGES FROM LIDAR DATA TO OPTIMIZE THE DIGITAL SURFACE MODEL FOR TRUE ORTHOPHOTO GENERATION

2018 ◽  
Vol XLII-2 ◽  
pp. 1199-1205
Author(s):  
E. Widyaningrum ◽  
R. C. Lindenbergh ◽  
B. G. H. Gorte ◽  
K. Zhou
Keyword(s):  
Point Clouds ◽  
Geometric Distortion ◽  
Surface Model ◽  
Digital Surface Model ◽  
Building Roof ◽  
Urban Scenes ◽  
Data Driven Approach ◽  
Edge Points ◽  
Scan Data ◽  
Different Characteristics

Various kinds of urban applications require true orthophotos. True orthophoto generation requires a DSM (Digital Surface Model) to project the photo orthogonally and minimize geometric distortion due to topographic variance. DSMs are often generated from airborne laser scan data. In urban scenes, DSM data may fail to deliver sharp and straight building roof edges. This will affect the quality of the resulting orthophotos. Therefore, it is necessary to incorporate good quality building outlines as breaklines during DSM interpolation. This study proposes a data-driven approach to construct building roof outlines from LiDAR point clouds by a workflow consisting of the following steps: given roof segments, roof boundary points are extracted using a concave hull algorithm. Straight edges may be difficult to find in complex roof configurations. Therefore, two ingredients are combined. First, RanSAC corner point preselection, and second, DBSCAN-based clustering of edge points. The method is demonstrated on an area of &amp;plusmn;1.2&amp;thinsp;km<sup>2</sup> containing 42 buildings of different characteristics. A quality assessment shows that the proposed method is able to deliver 92&amp;thinsp;% of building lines with acceptable geometric accuracy in comparison to a building line in the base map.

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

2019 ◽  
Vol 11 (9) ◽  
pp. 1040 ◽  
Author(s):  
Haiqing He ◽  
Junchao Zhou ◽  
Min Chen ◽  
Ting Chen ◽  
Dajun Li ◽  
...  
Keyword(s):  
Rural Areas ◽  
Point Clouds ◽  
Feature Representation ◽  
Surface Model ◽  
Building Detection ◽  
Building Extraction ◽  
Superpixel Segmentation ◽  
Convolutional Networks ◽  
3D Point Clouds ◽  
Uav Images

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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