Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix

نویسندگان

چکیده

Aiming at the problem of misdetection caused by traditional texture characteristic extraction model, which does not describe correlation among multiple bands, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. First, divided into multi-scale objects graph-based superpixel segmentation, and optimal scale determined overall goodness F-measure (OGF). Then, except for spectral features, multi-channel features (CCM) are extracted to consider bands. To accurately find representative overcome impact feature redundancy, cumulative backward search strategy (CBSS) further designed. Finally, completed inputting difference dual time points trained random forest model. Taking Shenzhen Dapeng as study areas, with Sentinel-2 Skysat images under different spatial resolutions, forest–bareland type example, effectiveness proposed algorithm verified qualitative quantitative analyses. They show that can obtain higher accuracy than without band correlation.

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2023

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app13116748