A New Stereo Pair Disparity Index (SPDI) for Detecting Built-Up Areas from High-Resolution Stereo Imagery

Feifei Peng,Pengcheng Liu,Huayi Wu,Le Wang,Jianya Gong

doi:10.3390/rs9060633

Abstract

Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slopes and building heights extracted from auxiliary data, such as Digital Surface Models (DSMs) however, can improve the results. Stereo imagery incorporates height information unlike single remotely sensed images. In this study, a new Stereo Pair Disparity Index (SPDI) for indicating built-up areas is calculated from stereo-extracted disparity information. Further, a new method of detecting built-up areas from stereo pairs is proposed based on the SPDI, using disparity information to establish the relationship between two images of a stereo pair. As shown in the experimental results for two stereo pairs covering different scenes with diverse urban settings, the SPDI effectively differentiates between built-up and non-built-up areas. Our proposed method achieves higher accuracy built-up area results from stereo images than the traditional method for single images, and two other widely-applied DSM-based methods for stereo images. Our approach is suitable for spaceborne and airborne stereo pairs and triplets. Our research introduces a new effective height feature (SPDI) for detecting built-up areas from stereo imagery with no need for DSMs.

Highlights

Identifying built-up areas is an essential task for government agencies facing the complex and ever changing demands of city planning [1,2,3]
Areas filled with pale blue color in subfigures c and d of Figures 10 and 11 are Stereo Pair Disparity Index (SPDI)-based built-up area results superposed on SPDI image and
The SPDI does not suffer from the influence of within-class spectral variation and between-class spectral confusion in remotely sensing imagery [10], which degrades the performance of built-up area detection when using planar texture, shape, and spectral features (e.g., Gabor [22])

Summary

Introduction

Identifying built-up areas is an essential task for government agencies facing the complex and ever changing demands of city planning [1,2,3]. Built-up areas are usually obtained from remotely sensed imagery using planar texture, shape, and spectral features [5,6,7], such as the Pantex [8,9]. Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades built-up detection performance [10]; performance can be increased by using height information from Light Detection and Ranging (LiDAR) data or stereo imagery. Stereo imagery incorporates both planar and height information. There is no need for auxiliary height data (e.g., LiDAR data) when detecting built-up areas from stereo imagery

Methods

Results

Conclusion