SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing

📘 Introduction

This repository contains the official implementation of our paper "SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing". Our work introduces:

• A novel task: CSIST Unmixing, which aims to detect [all targets in the form of sub-pixel localization from a highly dense CSIST group].
• A new dataset: SeqCSIST, specifically designed for [multi-frame CSIST Umixing].
• An End-to-End Framework: Our approach outperforms existing methods by [].

🗂 Dataset

• Number of samples: [100,000 frames organized into 5,000 random trajectories]
• Download: []

🔧 Model

Our model consists of three main modules:

• [Sparsity-driven Feature Extraction module]: []
• [Positional Encoding module]: []
• [Temporal Deformable Feature Alignment (TDFA) module]: []

🏗 Architecture

⚙ Installation

To set up the environment, run:

conda env create -f environment.yml
conda activate speed
mim install mmcv==2.0.1

🚀 Training

To train the model, run:

CUDA_VISIBLE_DEVICES=0,1,2,3 tools/dist_train.sh configs/configs/DeRefNet.py 4

🎯 Evaluation

To evaluate on the test set, run:

🏆 Results

Our method achieves state-of-the-art performance on SeqCSIST Task

📊 Comparison with state-of-the-art methods

---

Method	Params	FLOPs	CSO-mAP	AP₀₅	AP₁₀	AP₁₅	AP₂₀	AP₂₅
Traditional Optimization
ISTA	-	398.57 M	10.72	0.14	1.97	8.74	18.22	24.53
BID	-	10.89 M	14.40	0.00	3.00	13.00	26.00	30.00
Image Super-Resolution
SRCNN	15.84 K	0.35 G	49.64	1.40	16.30	51.20	85.00	94.30
GMFN	2.80 M	27.53 G	50.94	0.70	11.90	51.20	92.10	98.80
DBPN	1.96 M	4.75 G	50.40	0.80	12.50	51.20	90.00	97.40
SRGAN	35.31 M	40.274 G	26.96	0.30	3.90	19.40	46.90	64.30
BSRGAN	36.06 M	0.266 T	33.21	0.40	6.10	27.50	57.20	74.90
ESRGAN	50.45 M	0.375 T	36.86	0.40	6.00	30.30	66.80	80.70
RDN	22.31 M	53.97 G	49.61	0.70	10.60	48.20	90.40	98.20
EDSR	0.39 M	0.99 G	50.19	0.60	10.30	48.80	92.20	99.00
ESPCN	54.75 M	22.73 K	47.18	1.60	15.30	46.60	80.30	92.00
TDAN	0.59 M	2.179 G	47.96	0.50	8.60	43.80	89.30	97.50
Deep Unfolding
LIHT	21.10 M	0.42 G	6.36	0.10	1.00	4.30	10.40	16.00
LAMP	2.13 M	86.97 G	9.09	0.10	1.50	6.50	15.00	22.30
ISTA-Net	0.17 M	4.09 G	48.95	0.70	11.20	49.70	87.70	95.40
FISTA-Net	74.60 K	6.02 G	50.61	1.00	12.60	51.40	90.70	97.30
ISTA-Net+	0.38 M	7.70 G	51.02	1.00	13.70	52.70	90.40	93.70
ISTA-Net++	0.76 M	16.54 G	50.50	0.70	10.40	49.20	92.8	99.40
LISTA	21.10 M	0.42 G	9.39	0.10	1.70	6.90	15.40	22.70
USRNet	1.07 M	11.26 G	49.25	0.70	9.80	46.60	91.20	98.90
TiLISTA	2.22 M	86.97 M	13.52	0.20	2.10	9.50	22.60	33.30
RPCANet	0.68 M	14.81 G	47.17	0.70	10.20	44.50	84.60	95.90
DeRefNet (Ours)	0.89 M	15.70 G	51.55	1.00	14.40	54.90	90.40	97.10

🎥 Visualization

🔍 Citation

If you find this work useful, please cite our paper:

@article{zhai2025seqcsist,
  title={SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing},
  author={Ximeng Zhai, Bohan Xu, Yaohong Chen, Hao Wang, Kehua Guo, Yimian Dai},
  journal={ArXiv/IEEE Transactions on Geoscience and Remote Sensing},
  year={2025}
}