Building Detection Using Enhanced HOG–LBP Features and Region Refinement Processes

Abstract

Building detection from 2D high-resolution satellite images is a computer vision, photogrammetry and remote sensing task that has arisen in the last decades with the advances in sensors technology and can be utilised in several applications that require the creation of urban maps or the study of urban changes. However, the variety of irrelevant objects that appear in an urban environment and resemble buildings and the significant variations in the shape and generally the appearance of buildings render building detection a quite demanding task. As a result, automated methods that can robustly detect buildings in satellite images are necessary. To this end, we propose a building detection method that consists of two modules. The first module is a feature detector that extracts Histograms of Oriented Gradients (HOG) and Local Binary Patterns (LBP) from image regions. Using a novel approach, a Support Vector Machine (SVM) classifier is trained with the introduction of a special denoising distance measure for the computation of distances between HOG-LBP descriptors before their classification to the building or non-building class. The second module consists of a set of region refinement processes that employs the output of the HOG-LBP detector in the form of detected rectangular image regions. Image segmentation is performed and a novel building recognition methodology is proposed to accurately identify building regions, while simultaneously discard false detections of the first module of the proposed method. We demonstrate that the proposed methodology can robustly detect buildings from satellite images and outperforms state-of-the-art building detection methods.

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

“…Several of the practical applications are based on VHR remote sensing imagery classification at the pixel level [5][6][7][8], also defined as semantic segmentation. Semantic segmentation of remote sensing imagery aims to classify every pixel into a given category, and it is an important task for understanding and inferring objects [9,10] and the relationships between spatial objects in a scene [11].…”

Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index

“…Buildings are the places where human beings live, work, and recreate [1]. The distribution of buildings is useful in many applications such as disaster assessment, urban planning, and environmental monitoring [2,3], and the precise location of buildings can also help municipalities in their efforts to better assist and protect their citizens [4]. Therefore, it is very important to accurately detect buildings.…”

Object 3D Reconstruction Based on Photometric Stereo and Inverted Rendering

“…The reconstruction is highly applicable in a number of fields as it provides the ability to understand 3D scenes and objects on basis of 2D images. The applications ranging from robotics and automated industrial quality inspection over human-machine interaction [6] (example action, gesture and face recognition), satellite 3D data analysis [25], to movies and architectural applications [17]. Additionally, the method is commonly used to analyse the surfaces of a celestial object, such as the Moon [18].…”