Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images

Change detection in very-high-spatial-resolution (VHR) remote sensing images is a very challenging area with applicability in many problems ranging from damage assessment to land management and environmental monitoring. In this study, we investigated the change detection problem associated with analysing the vegetation corresponding to crops and natural ecosystems over VHR multispectral and hyperspectral images obtained by sensors onboard drones or satellites. The challenge of applying change detection methods to these images is the similar spectral signatures of the vegetation elements in the image. To solve this issue, a consensus multi-scale binary change detection technique based on the extraction of object-based features was developed. With the objective of capturing changes at different granularity levels taking advantage of the high spatial resolution of the VHR images and, as the segmentation operation is not well defined, we propose to use several detectors based on different segmentation algorithms, each applied at different scales. As the changes in vegetation also present high variability depending on capture conditions such as illumination, the use of the CVA-SAM applied at the segment level instead of at the pixel level is also proposed. The results revealed the effectiveness of the proposed approach for identifying changes over land cover vegetation images with different types of changes and different spatial and spectral resolutions.

Automated summary

In this study, the change detection problem in very-high-spatial-resolution remote sensing images was investigated, focusing on the vegetation corresponding to crops and natural ecosystems. A consensus multi-scale binary change detection technique based on object-based features extraction was developed to address the challenge of similar spectral signatures of vegetation elements. Different detectors based on various segmentation algorithms were utilized at different scales to capture changes at different granularity levels. The proposed approach, including the use of CVA-SAM at the segment level, demonstrated effectiveness in identifying changes over land cover vegetation images with different types, spatial and spectral resolutions.