RT Journal Article
T1 Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images
A1 Cardama Santiago, Francisco Javier
A1 Blanco Heras, Dora
A1 Argüello Pedreira, Francisco
K1 Multispectral
K1 Change detection
K1 Change vector analysis
K1 Superpixel segmentation
K1 Multi-scale;
K1 Data fusion
K1 Consensus
K1 Vegetation
AB 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.
PB MDPI
SN 2072-4292
YR 2023
FD 2023-06-01
LK https://hdl.handle.net/10347/38545
UL https://hdl.handle.net/10347/38545
LA eng
NO Cardama, F. J., Heras, D. B., & Argüello, F. (2023). Consensus techniques for unsupervised binary change detection using multi-scale segmentation detectors for land cover vegetation images. Remote Sensing, 15(11), 2889.
NO Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (PID2019-104834GB-I00, TED2021-130367B-I00, y FJC2021-046760-I)
NO Unión Europea (ERDF y NextGenerationEU PRTR)
NO Consellería de Cultura, Educación, Formación Profesional e Universidades, Xunta de Galicia  (ED431G-2019/04 y ED431C-2022/16)
NO Junta de Castilla y León (VA226P20 (PROPHET-II))
DS Minerva
RD 24 abr 2026