Change Detection On Whu Cd

Métriques

IoU

Overall Accuracy

Precision

Recall

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	F1	IoU	Overall Accuracy	Precision	Recall	Paper Title	Repository
CDMaskFormer	91.56	84.44	99.23	92.25	90.89	Rethinking Remote Sensing Change Detection With A Mask View	-
DDLNet	90.56	82.75	99.13	91.56	90.03	DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning	-
Tiny-CD	91.05	83.57	99.10	92.68	89.47	TINYCD: A (Not So) Deep Learning Model For Change Detection	-
HANet	88.16	78.82	99.16	88.30	88.01	HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images	-
LRNet	92.51	86.06	99.47	95.11	90.04	LRNet: Change detection of high-resolution remote sensing imagery via strategy of localization-then-refinement	-
CGNet	92.59	86.21	99.48	94.47	90.79	Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery	-
RSM-CD	91.87	84.96	-	93.37	90.42	RS-Mamba for Large Remote Sensing Image Dense Prediction	-
T-UNet	91.77	-	99.42	95.44	88.37	T-UNet: Triplet UNet for Change Detection in High-Resolution Remote Sensing Images	-
CLAFA-LWGANet L2	95.24	90.92	-	96.51	-	LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks	-
BiFA	94.37	89.34	99.56	95.15	93.60	BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment
DTCDSCN	89.75	81.40	-	90.15	89.35	Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model	-
DDPM-CD	92.65	-	99.42	-	-	DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection	-
ChangeMamba	94.19	89.02	99.58	96.18	92.23	ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model	-
Dsfer-Net	92.58	86.18	99.46	94.17	91.04	Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks	-
C2FNet	94.36	89.33	99.56	96.57	92.26	C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images	-
STNet	87.46	77.72	98.85	87.84	87.08	STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images	-
HCGMNet	92.08	85.33	99.45	93.93	90.31	HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection	-
SRC-Net	92.06	85.28	99.30	92.57	91.55	SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection	-
RFL-CDNet	91.39	-	-	91.33	91.46	RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning	-
CDMamba	93.76	88.26	99.51	95.58	92.01	CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection	-

0 of 20 row(s) selected.