The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B5-2022
02 Jun 2022
 | 02 Jun 2022


E. J. Dippold and F. Tsai

Keywords: Feature-based Matching Very High-Resolution Satellite Imagery, Normalized Differential Vegetation Index (NDVI), Image Matching, Stereo Matching

Abstract. The automatic establishment of image relationship between oblique images can be a challenging task. Feature based image matching (FBM) establishes this relationship by detecting and matching corresponding feature points. A robust matching is beneficial for many tasks including reconstruction, mapping and localization. The need of automatic processing of remotely sensed data, like very highresolution (VHR) satellite imagery, increased over the years. Furthermore, green vegetation and water are changing the physical properties with respect to the amount of light emitted, season and pollution. In addition, with human interaction, the change in appearance increases. The Normalized Differential Vegetation Index (NDVI) is a well-known and studied index in order to detect healthy green vegetation. The Normalized Differential Water Index (NDWI) can help identify water areas in an image. They can be used to preliminarily segment images into different categories for later process. This study proposes a novel framework to explore the potential of feature based stereo matching for very high-resolution satellite imagery with segmentation. The proposed framework will perform first, image segmentation with NDVI and NDWI on stereo VHR satellite image pairs. Then, classification by threshold to detect healthy green vegetation, water and image frame. Features within these three classes are masked out and kept from being processed during the feature matching step. The idea is that, features in these classes are easy to cause miss matching because they are more prone to be affected by different image and environmental conditions in the stereo image pairs. As a result, the amount of miss matches can be reduced on average by 7.5%. Furthermore, the segmentation decreases the total amount of detected features by 13.71%, so that the processing time decreases. This study has successfully proven that segmentation can lead to improved stereo matching. In future, segmentation driven can be utilized by AI matching processes as well as traditionally photogrammetric or computer vision tasks.