Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images
Open Access
- 20 June 2021
- journal article
- research article
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 10 (6), 420
- https://doi.org/10.3390/ijgi10060420
Abstract
Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.Keywords
Funding Information
- National Key Research and Development Program of China (2017YFB0503500)
- Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19040402)
This publication has 52 references indexed in Scilit:
- Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imageryRemote Sensing of Environment, 2011
- Segmentation performance evaluation for object-based remotely sensed image analysisInternational Journal of Remote Sensing, 2010
- Object based image analysis for remote sensingISPRS Journal of Photogrammetry and Remote Sensing, 2010
- Optimization in multi‐scale segmentation of high‐resolution satellite images for artificial feature recognitionInternational Journal of Remote Sensing, 2007
- Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, ChinaInternational Journal of Remote Sensing, 2006
- Unsupervised Performance Evaluation of Image SegmentationEURASIP Journal on Advances in Signal Processing, 2006
- Spectral discrimination of vegetation types in a coastal wetlandRemote Sensing of Environment, 2003
- Quantitative Geography: Perspectives on Spatial Data Analysis, by A. S. Fotheringham, C. Brunsdon, and M. CharltonGeographical Analysis, 2001
- Normalized cuts and image segmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
- Watersheds in digital spaces: an efficient algorithm based on immersion simulationsIEEE Transactions on Pattern Analysis and Machine Intelligence, 1991