ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model

Convolutional neural networks (CNNs) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN is constrained by a limited receptive field that may hinder their ability to capture broader spatial c...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-20
Hauptverfasser:	Chen, Hongruixuan, Song, Jian, Han, Chengxi, Xia, Junshi, Yokoya, Naoto
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Benchmark testing Binary change detection (BCD) building damage assessment (BDA) Change detection Computer architecture Damage Damage assessment Datasets Feature extraction Information processing Mamba Natural language processing Neural networks optical high-resolution images Receptive field Remote sensing semantic change detection (SCD) Semantics Source code Spatial discrimination learning Spatiotemporal data spatiotemporal relationship state space model (SSM) State space models Task analysis Task complexity Transformers Visual discrimination learning
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