  Remote sensing image change detection aims to identify the differences
between images acquired at different times in the same area. It is widely used
in land management, environmental monitoring, disaster assessment and other
fields. Currently, most change detection methods are based on Siamese network
structure or early fusion structure. Siamese structure focuses on extracting
object features at different times but lacks attention to change information,
which leads to false alarms and missed detections. Early fusion (EF) structure
focuses on extracting features after the fusion of images of different phases
but ignores the significance of object features at different times for
detecting change details, making it difficult to accurately discern the edges
of changed objects. To address these issues and obtain more accurate results,
we propose a novel network, Triplet UNet(T-UNet), based on a three-branch
encoder, which is capable to simultaneously extract the object features and the
change features between the pre- and post-time-phase images through triplet
encoder. To effectively interact and fuse the features extracted from the three
branches of triplet encoder, we propose a multi-branch spatial-spectral
cross-attention module (MBSSCA). In the decoder stage, we introduce the channel
attention mechanism (CAM) and spatial attention mechanism (SAM) to fully mine
and integrate detailed textures information at the shallow layer and semantic
localization information at the deep layer.
