Classification of Urban Building Type from High Spatial Resolution Remote Sensing Imagery Using Extended MRS and Soft BP Network

• Main Authors: Junfei Xie, Jianhua Zhou
• Format: Article
• Language: English
• Published: IEEE 2017-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Back propagation network; building types; multiresolution segmentation (MRS); soft classification; urban area
• Online Access: https://ieeexplore.ieee.org/document/8007414/

This study presents a new approach for classification of building type in complex urban scene. The approach consists of two parts: extended multiresolution segmentation (EMRS) and soft classification using BP network (SBP). The technology scheme is referred to here as EMRS-SBP. EMRS is used to guide the design of descriptor. A descriptor is a feature expression or a symbolized algorithm to systematically promote the expressing capability of image features. A classifier can perform far better to discern complex pattern of combining pixels working in an EMRS-based feature space constructed by a number of such descriptors. SBP serves as a classifier model to generate natural clusters of member which refers to here as both pixels and image patches. Class-mark ensured member is denoted as sure member and the rest as unsure (fuzzy) members. The latter can be relabeled through recursive defuzzifying according to the information carried by the gradually increased sure members. By using EMRS-SBP, three building types, i.e., old-fashioned courtyard dwellings, multistorey residential buildings, and high-rise buildings, can be accurately classified from high spatial resolution imagery in a feature space constructed with fifteen descriptors including nine EMRS-based ones. There is evidence that the mean overall accuracy using SBP in the EMRS-based feature space is 19.8% higher than that using the hard classification with BP network in a single resolution segmentation space and meanwhile, the mean kappa statistic value (κ) is 25.1% higher.