  In the later training stages, further improvement of the models ability to
determine changes relies on how well the change detection (CD) model learns
hard cases; however, there are two additional challenges to learning hard case
samples: (1) change labels are limited and tend to pointer only to foreground
targets, yet hard case samples are prevalent in the background, which leads to
optimizing the loss function focusing on the foreground targets and ignoring
the background hard cases, which we call imbalance. (2) Complex situations,
such as light shadows, target occlusion, and seasonal changes, induce hard case
samples, and in the absence of both supervisory and scene information, it is
difficult for the model to learn hard case samples directly to accurately
obtain the feature representations of the change information, which we call
missingness. We propose a Siamese foreground association-driven hard case
sample optimization network (HSONet). To deal with this imbalance, we propose
an equilibrium optimization loss function to regulate the optimization focus of
the foreground and background, determine the hard case samples through the
distribution of the loss values, and introduce dynamic weights in the loss term
to gradually shift the optimization focus of the loss from the foreground to
the background hard cases as the training progresses. To address this
missingness, we understand hard case samples with the help of the scene
context, propose the scene-foreground association module, use potential remote
sensing spatial scene information to model the association between the target
of interest in the foreground and the related context to obtain scene
embedding, and apply this information to the feature reinforcement of hard
cases. Experiments on four public datasets show that HSONet outperforms current
state-of-the-art CD methods, particularly in detecting hard case samples.
