HyperAI초신경

홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

한국어

HyperAI초신경

Change Detection On S2Looking

평가 지표

F1

F1-Score

IoU

KC

OA

Precision

Recall

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	F1	F1-Score	IoU	KC	OA	Precision	Recall	Paper Title	Repository
HCGMNet	63.87	63.87	46.91	63.48	99.22	72.51	57.06	HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection
BD-MSA	-	64.08	47.14	-	-	-	-	BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation	-
LSKNet-S	-	67.52	50.96	-	-	71.90	63.64	LSKNet: A Foundation Lightweight Backbone for Remote Sensing
CGNet	-	64.33	47.41	63.93	99.20	70.18	59.38	Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery
Changer (Ex, ResNet-18)	-	66.20	-	-	-	-	-	Changer: Feature Interaction is What You Need for Change Detection
HANet	58.54	58.54	41.38	58.05	99.04	61.38	55.94	HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images
ChangeStar(1x96, MiT-b1, Changen-90k)	-	67.9	-	-	-	-	-	Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process
ChangeStar(1x96, ResNet-18, Changen-90k)	-	67.1	-	-	-	-	-	Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process
C2FNet	62.83	62.83	45.80	62.44	99.22	74.84	54.14	C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images

0 of 9 row(s) selected.