Object-Oriented and Deep-Learning-Based High-Resolution Mapping from Large Remote Sensing Imagery

Abstract

Land use and land cover (LULC) mapping is a basic research topic in geography. In deep-learning (DL)-based LULC mapping, there are primarily the following issues: training and testing samples for DL are typically annotated by indoor visual interpretation without field surveys; remotely sensed scene classification based on DL typically lacks fine geometric boundaries for ground objects; historical big data (HBD) (e.g., vector data) are underutilized in DL; studies of large-scale remote sensing mapping (LSRSM) using DL are rare. To solve above issues, this paper proposes an object-oriented (i.e., polygon-based) and DL-based (OODLB) image classification method assisted by HBD for LSRSM to serve for monitoring the soil erosion and water loss (SEWL) in the Yangtze River Basin that includes the following steps: (1) using HBD and OpenStreetMap data, ground objects are vectorized; (2) a remote sensing interpretation key database is established by field surveys and data augmentation; (3) object-oriented (i.e., polygon-based) and Inception-ResNet-V2-based LULC mapping is performed; (4) DL-based classification results are updated by man-machine mutual verification. The experimental results of one county of the Yangtze River Basin show state-of-the-art performance with an overall accuracy of 90.20% in comparison of 75.60% for eCognition. It provides an excellent framework for OODLB large-scene mapping.