Multi-Temporal Scene Classification and Scene Change Detection With Correlation Based Fusion

نویسندگان

چکیده

Classifying multi-temporal scene land-use categories and detecting their semantic scene-level changes for imagery covering urban regions could straightly reflect the transitions. Existing methods change detection rarely focus on temporal correlation of bi-temporal features, are mainly evaluated small scale datasets. In this work, we proposed a CorrFusion module that fuses highly correlated components in feature embeddings. We firstly extracts deep representations inputs with convolutional networks. Then extracted features will be projected into lower dimension space to computed instance-level correlation. The cross-temporal fusion performed based module. final classification obtained softmax activation layers. objective function, introduced new formulation calculating detailed derivation backpropagation gradients is also given paper. Besides, presented much larger dataset conducted experiments dataset. experimental results demonstrated our remarkably improve results.

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ژورنال

عنوان ژورنال: IEEE transactions on image processing

سال: 2021

ISSN: ['1057-7149', '1941-0042']

DOI: https://doi.org/10.1109/tip.2020.3039328