  The existing SSCL of RSI is built based on constructing positive and negative
sample pairs. However, due to the richness of RSI ground objects and the
complexity of the RSI contextual semantics, the same RSI patches have the
coexistence and imbalance of positive and negative samples, which causing the
SSCL pushing negative samples far away while pushing positive samples far away,
and vice versa. We call this the sample confounding issue (SCI). To solve this
problem, we propose a False negAtive sampLes aware contraStive lEarning model
(FALSE) for the semantic segmentation of high-resolution RSIs. Since the SSCL
pretraining is unsupervised, the lack of definable criteria for false negative
sample (FNS) leads to theoretical undecidability, we designed two steps to
implement the FNS approximation determination: coarse determination of FNS and
precise calibration of FNS. We achieve coarse determination of FNS by the FNS
self-determination (FNSD) strategy and achieve calibration of FNS by the FNS
confidence calibration (FNCC) loss function. Experimental results on three RSI
semantic segmentation datasets demonstrated that the FALSE effectively improves
the accuracy of the downstream RSI semantic segmentation task compared with the
current three models, which represent three different types of SSCL models. The
mean Intersection-over-Union on ISPRS Potsdam dataset is improved by 0.7\% on
average; on CVPR DGLC dataset is improved by 12.28\% on average; and on
Xiangtan dataset this is improved by 1.17\% on average. This indicates that the
SSCL model has the ability to self-differentiate FNS and that the FALSE
effectively mitigates the SCI in self-supervised contrastive learning. The
source code is available at https://github.com/GeoX-Lab/FALSE.
