Automatic detection of residential buildings using LIDAR data and multispectral imagery
نویسندگان
چکیده
This paper presents an automatic building detection technique using LIDAR data and multispectral imagery. Two masks are obtained from the LIDAR data: a ‘primary building mask’ and a ‘secondary building mask’. The primary building mask indicates the void areas where the laser does not reach below a certain height threshold. The secondary building mask indicates the filled areas, from where the laser reflects, above the same threshold. Line segments are extracted from around the void areas in the primary building mask. Line segments around trees are removed using the normalized difference vegetation index derived from the orthorectified multispectral images. The initial building positions are obtained based on the remaining line segments. The complete buildings are detected from their initial positions using the two masks and multispectral images in the YIQ colour system. It is experimentally shown that the proposed technique can successfully detect urban residential buildings, when assessed in terms of 15 indices including ∗Corresponding author Preprint submitted to ISPRS J of Photogrammetry and Remote Sensing August 5, 2010 completeness, correctness and quality.
منابع مشابه
Building Detection from Multispectral Imagery and Lidar Data Employing a Threshold-free Evaluation System
This paper presents an automatic system for the detection of buildings from LIDAR data and multispectral imagery, which employs a threshold-free evaluation system that does not involve any thresholds based on human choice. Two binary masks are obtained from the LIDAR data: a ‘primary building mask’ and a ‘secondary building mask’. Line segments are extracted from around the primary building mas...
متن کاملSatellite Imagery Classification with Lidar Data
This paper shows the potential of LIDAR for extracting buildings and other objects from medium resolution satellite imagery. To that end, the study integrated multispectral and LIDAR elevation data in a single imagery file and then classified it using the Support Vector Machine. To determine the method’s potential, the study used a SPOT5 satellite from an area situated southeast of Madrid, Spai...
متن کاملDetecting Buildings and Roof Segments by Combining LIDAR Data and Multispectral Images
A method for the automatic detection of buildings and their roof planes from LIDAR data and multispectral images is presented. For building detection, a classification technique is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. The detection of roof planes is based on a region growing algorithm applied to the LIDAR data, the s...
متن کاملBuilding Detection Using LIDAR Data and Multispectral Images
A method the automatic detection of buildings from LIDAR data and multi-spectral images is presented. A classification technique using various cues derived from these data is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. Both first and last pulse data and the normalised difference vegetation index are used in that process. We...
متن کاملAerial Images and Lidar Data Fusion for Automatic Feature Extraction Using the Self-organizing Map (som) Classifier
This paper presents work on the development of automatic feature extraction from multispectral aerial images and lidar data based on test data from two different study areas with different characteristics. First, we filtered the lidar point clouds to generate a Digital Terrain Model (DTM) using a novel filtering technique based on a linear first-order equation which describes a tilted plane sur...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010