Integrated Local Features to Detect Building Locations in High-Resolution Satellite Imagery

A building is one of the most important man-made objects forming part of a class of interest to photogrammetry and remote-sensing researchers that many studies in recent years have focused on extracting the locations of buildings, especially from high-resolution images, which are widely used to detect geospatial targets such as buildings. Conventional building-detection methods apply various image-processing techniques based on pattern recognition, classification and neural network methods, all of which have some problems, such as eliminating small buildings, needing a training sample, detecting trees as a building and mixing buildings with other classes. Local features are significant algorithm invariants to viewpoint and illumination changes that have been made over the past decade, to find local images of interesting structures. Each local feature algorithm has distinct characteristics that can help in building detection. In this study, a novel, integrated method is presented for detecting building locations from high-resolution satellite imagery, based on local features. In the proposed method, three types of local features, i.e. point features, circular blob-like features and regional elliptical features, are applied in order to describe building areas on high-resolution images. Considering the different characteristics of local feature extraction algorithms, such as differences in the numbers, densities, sizes and shapes of extracted features, a weighted integrated approach based on a multi-kernel probability density function has been developed. The proposed method does not require image information in different bands, as it works on black-and-white images. A blunder-detection method, based on an orientation histogram, is also used to refine the building extraction process. The proposed method is successfully applied to the detection of buildings on various satellite images, and the results demonstrate its capability in terms of recall, precision and F-score.

[1]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[2]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Tamás Szirányi,et al.  Improved Harris Feature Point Set for Orientation-Sensitive Urban-Area Detection in Aerial Images , 2013, IEEE Geoscience and Remote Sensing Letters.

[4]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[5]  Hamid Abrishami Moghaddam,et al.  Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Giorgos Mountrakis,et al.  A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research , 2016 .

[7]  Nikolaos Grammalidis,et al.  Building Detection Using Enhanced HOG–LBP Features and Region Refinement Processes , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[9]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[10]  X. Briottet,et al.  Shadow detection in very high spatial resolution aerial images: A comparative study , 2013 .

[11]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[12]  Dietmar Saupe,et al.  A morphological approach for distinguishing texture and individual features in images , 2014, Pattern Recognit. Lett..

[13]  Cem Ünsalan,et al.  A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Ali Mohammadzadeh,et al.  A Novel Building and Tree Detection Method From LiDAR Data and Aerial Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  C. Fraser,et al.  Building detection in complex scenes thorough effective separation of buildings from trees , 2012 .

[17]  Siyang Chen,et al.  Automatic building extraction from LiDAR data fusion of point and grid-based features , 2017 .

[18]  Ying Yu,et al.  Accurate Urban Area Detection in Remote Sensing Images , 2015, IEEE Geoscience and Remote Sensing Letters.

[19]  Salman Ahmadi,et al.  An improved snake model for automatic extraction of buildings from urban aerial images and LiDAR data , 2010, Comput. Environ. Urban Syst..

[20]  Jianhua Liu,et al.  Scale computation on high spatial resolution remotely sensed imagery multi-scale segmentation , 2017 .

[21]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[22]  Tamás Szirányi,et al.  Harris function based active contour external force for image segmentation , 2012, Pattern Recognit. Lett..

[23]  Mehdi Mokhtarzade,et al.  Photogrammetric Engineering & Remote Sensing , 2022 .

[24]  Tamás Szirányi,et al.  Orientation-selective building detection in aerial images , 2015 .

[25]  Cem Ünsalan,et al.  Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Amin Sedaghat,et al.  Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Vincent Mazet,et al.  Shape-Based Building Detection in Visible Band Images Using Shadow Information , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Xin Huang,et al.  A Multidirectional and Multiscale Morphological Index for Automatic Building Extraction from Multispectral GeoEye-1 Imagery , 2011 .

[29]  T. Szirányi,et al.  ORIENTATION BASED BUILDING OUTLINE EXTRACTION IN AERIAL IMAGES , 2012 .

[30]  Hamid Ebadi,et al.  AUTOMATIC EXTRACTION OF BUILDING FEATURES FROM HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS , 2007 .

[31]  Cem Unsalan.  Gradient-Magnitude-Based Support Regions in Structural Land Use Classification , 2006 .

[32]  Jian Liang,et al.  A Bottom-Up/Top-Down Hybrid Algorithm for Model-Based Building Detection in Single Very High Resolution SAR Image , 2017, IEEE Geoscience and Remote Sensing Letters.

[33]  Dongmei Chen,et al.  Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .

[34]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[35]  Dong-Chen He,et al.  Automatic fuzzy object-based analysis of VHSR images for urban objects extraction , 2013 .

[36]  Li Sun,et al.  Rural Building Detection in High-Resolution Imagery Based on a Two-Stage CNN Model , 2017, IEEE Geoscience and Remote Sensing Letters.