Change Detection On Whu Cd

IoU

Overall Accuracy

Precision

Recall

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	F1	IoU	Overall Accuracy	Precision	Recall
rethinking-remote-sensing-change-detection	91.56	84.44	99.23	92.25	90.89
ddlnet-boosting-remote-sensing-change	90.56	82.75	99.13	91.56	90.03
tinycd-a-not-so-deep-learning-model-for	91.05	83.57	99.10	92.68	89.47
hanet-a-hierarchical-attention-network-for	88.16	78.82	99.16	88.30	88.01
lrnet-change-detection-of-high-resolution	92.51	86.06	99.47	95.11	90.04
change-guiding-network-incorporating-change	92.59	86.21	99.48	94.47	90.79
rs-mamba-for-large-remote-sensing-image-dense	91.87	84.96	-	93.37	90.42
t-unet-triplet-unet-for-change-detection-in	91.77	-	99.42	95.44	88.37
lwganet-a-lightweight-group-attention	95.24	90.92	-	96.51	-
bifa-remote-sensing-image-change-detection	94.37	89.34	99.56	95.15	93.60
building-change-detection-for-remote-sensing	89.75	81.40	-	90.15	89.35
remote-sensing-change-detection-segmentation	92.65	-	99.42	-	-
changemamba-remote-sensing-change-detection	94.19	89.02	99.58	96.18	92.23
dsfer-net-a-deep-supervision-and-feature	92.58	86.18	99.46	94.17	91.04
c2f-semicd-a-coarse-to-fine-semi-supervised	94.36	89.33	99.56	96.57	92.26
stnet-spatial-and-temporal-feature-fusion	87.46	77.72	98.85	87.84	87.08
hcgmnet-a-hierarchical-change-guiding-map	92.08	85.33	99.45	93.93	90.31
src-net-bi-temporal-spatial-relationship	92.06	85.28	99.30	92.57	91.55
rfl-cdnet-towards-accurate-change-detection	91.39	-	-	91.33	91.46
cdmamba-remote-sensing-image-change-detection	93.76	88.26	99.51	95.58	92.01