Abstract
Abstract. Multi-temporal building change detection is one of the most essential major issues of photogrammetry and remote sensing at current stage, which is of great significance for wide applications as offering real estate indicators as well as monitoring urban environment. Although current photogrammetry methodologies could be applicated to 2-D remote sensing imagery for rectification with sensor parameters, multi-temporal aerial or satellite imagery is not adequate to offer spectral and textual features for building change detection. Alongside recent development of Dense Image Matching (DIM) technology, the acquisition of 3-D point cloud and Digital Surface Model (DSM) has been generally realized, which could be combined with imagery, making building change detection more effective with greater spatial structure and texture information. Over the past years, scholars have put forward vast change detection techniques including traditional and model-based solutions. Nevertheless, existing appropriate methodology combined with Neural Networks (NN) for accurate building change detection with multi-temporal imagery and DSM remains to be of great research focus currently due to the inevitable limitations and omissions of existing NN-based methods, which is of great research prospect. This study proposed a novel end-to-end model framework based on deep learning for pixel-level building change detection from high-spatial resolution aerial ortho imagery and corresponding DSM sharing same resolution, which is from the dataset of Tokyo whole area.
Highlights
Change detection is the process of identifying differentiations in the state of an objector phenomenon by observing multitemporally
With corresponding advantages and shortcomings of great divergence of existing methods, considering the requirements of building change detection research and practical application scenario, our study proposes a novel end-to-end change detection model based on neural network for urban ortho aerial imagery
The main objective of this study is to generate change map classified into three classes including new construction, demolishment and continuation by end-to-end model based on Feature Pyramid Network (FPN) with ortho aerial urban imagery
Summary
Change detection is the process of identifying differentiations in the state of an objector phenomenon by observing multitemporally. Within the applications by applying multi-temporal remote sensing imagery to derive timely information on the earth’s environment and human activities, most of scholars concentrated on natural environment related ones including monitoring of shifting cultivation, assessment of deforestation, study of changes in vegetation phenology, seasonal changes in pasture production, damage assessment, crop stress detection and so on(Singh, 1989). Urban constructed environment multi-temporal change detection including building construction, traffic construction, urban facilities and other infrastructures timely change is significant for urban activities monitoring, real estate market mastery, resident’s mobility and whole city development promotion. Our study will focus on the application scenario of urban construction change detection including building new construction, demolishment as well as continuation, which is aiming at urban construction legitimacy supervision and real estate commercial activity monitoring
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