自回归与反馈驱动的自适应矩形卷积全色锐化网络

doi:10.7527/S1000-6893.2025.32432

Abstract

Abstract:

To enhance spectral fidelity and spatial detail restoration in remote sensing pansharpening tasks， this paper proposes a deep pansharpening network based on an encoder-decoder architecture， named the Autoregressive and Feedback-Driven Adaptive Rectangular Convolution Network for Pansharpening （AFAR-Net）. The proposed network employs an autoregressive mechanism， where the output of the previous unit is used to optimize the current one， enabling progressive multi-level image reconstruction. Meanwhile， a feedback-driven fusion module is designed to efficiently integrate deep features across units， thereby enhancing spectral consistency. On the other hand， an adaptive convolutional residual block is introduced to flexibly adjust kernel sizes and shapes， strengthening the network’s ability to model and restore complex spatial structures. Finally， a lightweight fusion head is utilized to aggregate multi-level predictions， improving the stability of reconstruction. Experimental results on multiple remote sensing datasets demonstrate that the proposed network outperforms existing mainstream approaches in terms of spatial distortion， Spectral Angle Mapper （SAM）， and the Quality with No Reference （QNR） index， showing strong generalization ability and application potential.

Key words: remote sensing image, pansharpening, autoregressive, adaptive convolution, deep learning

CLC Number:

Shaohua DUAN, Chunjie ZHANG, Chuankai LIU, Xiaolong ZHENG, Jitao ZHANG. AFAR-Net: Autoregressive and feedback-driven adaptive rectangular convolution network for pansharpening[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(10): 532432.

Figures/Tables 12

Fig.1

Fig.2

Table 1

Pansharpening performance evaluation on WV3 Dataset （Mean ± Std）

方法	低分辨率指标			全分辨率指标
方法	SAM	ERGAS	Q	$D λ$	$D S$	QNR
BDSD-PC^［32］	5.429±1.823	4.698±1.617	0.829±0.097	0.062 5±0.023 5	0.073 0±0.035 6	0.870±0.053
CVPR19^［35］	5.207±1.574	5.484±1.505	0.764±0.088	0.029 7±0.005 9	0.041 0±0.013 6	0.931±0.018
LRTCFPan^［36］	4.737±1.412	4.315±1.442	0.846±0.091	0.017 6±0.006 6	0.052 8±0.025 8	0.931±0.031
DiCNN^［24］	3.593±0.762	2.673±0.663	0.900±0.087	0.036 2±0.011 1	0.046 2±0.017 5	0.920±0.026
FusionNet^［40］	3.325±0.698	2.467±0.645	0.904±0.090	0.023 9±0.009 0	0.036 4±0.013 7	0.941±0.020
DCFNet^［25］	3.038±0.585	2.165±0.499	0.913±0.087	0.018 7±0.007 2	0.033 7±0.005 4	0.948±0.012
LAGConv^［27］	3.104±0.559	2.300±0.613	0.910±0.091	0.036 8±0.014 8	0.041 8±0.015 2	0.923±0.025
HMPNet^［41］	3.063±0.577	2.229±0.545	0.916±0.087	0.018 4±0.007 3	0.053 0±0.055 5	0.930±0.011
CMT^［26］	2.994±0.607	2.214±0.516	0.917±0.085	0.020 7±0.008 2	0.037 0±0.007 8	0.943±0.014
CANNet^［44］	2.930±0.593	2.158±0.515	0.920±0.084	0.019 6±0.008 3	0.030 1±0.007 4	0.951±0.013
ARConv^［28］	2.885±0.590	2.139±0.528	0.921±0.083	0.014 6±0.005 9	0.027 9±0.006 8	0.958±0.010
AFAR-Net	2.860±0.582	2.113±0.525	0.921±0.084	0.015 2±0.006 3	0.027 5±0.005 4	0.958±0.010

Table 1

Table 2

Pansharpening performance evaluation on QB Dataset （Mean ± Std）

方法	低分辨率指标			全分辨率指标
方法	SAM	ERGAS	Q	$D λ$	$D S$	QNR
BDSD-PC^［32］	8.089±1.980	7.515±0.800	0.831±0.090	0.197 5±0.033 4	0.163 6±0.048 3	0.672±0.058
CVPR19^［35］	7.998±1.820	9.359±1.268	0.737±0.087	0.049 8±0.011 9	0.078 3±0.017 0	0.876±0.023
LRTCFPan^［36］	7.187±1.711	6.928±0.812	0.855±0.087	0.022 6±0.011 7	0.070 5±0.035 1	0.909±0.044
DiCNN^［24］	5.380±1.027	5.135±0.488	0.904±0.094	0.094 7±0.014 5	0.106 7±0.021 0	0.809±0.031
FusionNet^［40］	4.923±0.908	4.159±0.321	0.925±0.090	0.057 2±0.018 2	0.052 2±0.008 8	0.894±0.021
DCFNet^［25］	4.512±0.773	3.809±0.336	0.934±0.087	0.046 9±0.015 0	0.123 9±0.026 9	0.835±0.016
LAGConv^［27］	4.547±0.830	3.826±0.420	0.934±0.088	0.085 9±0.023 7	0.067 6±0.013 6	0.852±0.018
HMPNet^［41］	4.617±0.404	3.404±0.478	0.936±0.102	0.183 2±0.054 2	0.079 3±0.024 5	0.753±0.065
CMT^［26］	4.535±0.822	3.744±0.321	0.935±0.086	0.050 4±0.012 2	0.036 8±0.007 5	0.915±0.016
CANNet^［44］	4.507±0.835	3.652±0.327	0.937±0.083	0.037 0±0.012 9	0.049 9±0.009 2	0.915±0.012
ARConv^［28］	4.430±0.811	3.633±0.327	0.939±0.081	0.038 4±0.014 8	0.039 6±0.009 0	0.924±0.019
AFAR-Net	4.427±0.811	3.597±0.324	0.938±0.084	0.043 0±0.016 3	0.033 8±0.017 6	0.925±0.030

Table 2

Table 3

Pansharpening performance evaluation on GF2 Dataset （Mean ± Std）

方法	低分辨率指标			全分辨率指标
方法	SAM	ERGAS	Q	$D λ$	$D S$	QNR
BDSD-PC^［32］	1.681±0.360	1.667±0.445	0.892±0.035	0.075 9±0.030 1	0.154 8±0.028 0	0.781±0.041
CVPR19^［35］	1.598±0.353	1.877±0.448	0.886±0.028	0.030 7±0.012 7	0.062 2±0.010 1	0.909±0.017
LRTCFPan^［36］	1.315±0.283	1.301±0.313	0.932±0.033	0.032 5±0.026 9	0.089 6±0.014 1	0.881±0.023
DiCNN^［24］	1.053±2.231	1.081±0.254	0.959±0.010	0.036 9±0.013 2	0.099 2±0.013 1	0.868±0.016
FusionNet^［40］	0.974±2.212	0.988±0.222	0.964±0.009	0.035 0±0.012 4	0.101 3±0.013 4	0.867±0.018
DCFNet^［25］	0.872±0.169	0.784±0.146	0.974±0.009	0.024 0±0.011 5	0.065 9±0.009 6	0.912±0.012
LAGConv^［27］	0.786±0.148	0.687±0.113	0.981±0.008	0.028 4±0.013 0	0.079 2±0.013 6	0.895±0.020
HMPNet^［41］	0.803±0.141	0.564±0.099	0.981±0.020	0.081 9±0.049 9	0.114 6±0.012 6	0.813±0.049
CMT^［26］	0.753±0.138	0.648±0.109	0.982±0.007	0.022 5±0.011 6	0.043 3±0.009 6	0.935±0.014
CANNet^［44］	0.707±0.148	0.630±0.128	0.983±0.006	0.019 4±0.010 1	0.063 0±0.009 4	0.919±0.011
ARConv^［28］	0.698±0.149	0.626±0.127	0.983±0.007	0.018 9±0.009 7	0.051 5±0.009 9	0.931±0.012
AFAR-Net	0.683±0.147	0.610±0.129	0.984±0.006	0.027 7±0.026 4	0.037 5±0.010 6	0.936±0.020

Table 3

Fig.3

Fig.4

Table 4

Table 5

Ablation test results of autoregressive mechanism and adaptive rectangular convolution residual block on WV3 Dataset （Mean ± Std）

自回归机制	AR-RB	低分辨率指标			全分辨率指标
自回归机制	AR-RB	SAM	ERGAS	Q	$D λ$	$D S$	QNR
×	×	2.997±0.610	2.215±0.553	0.918±0.085	0.019 4±0.008 1	0.030 8±0.007 2	0.951±0.013
√	×	2.951±0.593	2.185±0.531	0.919±0.085	0.042 3±0.062 9	0.049 5±0.041 4	0.913±0.091
×	√	2.885±0.590	2.139±0.528	0.921±0.083	0.014 6±0.005 9	0.027 9±0.006 8	0.958±0.010
√	√	2.860±0.582	2.113±0.525	0.921±0.084	0.015 2±0.006 3	0.027 5±0.005 4	0.958±0.010

Table 5

Table 6

Ablation test results of FD-FM on WV3 Dataset （Mean ± Std）

FD-FM	低分辨率指标
FD-FM	SAM	ERGAS	Q
×	2.904 $±$ 0.594	2.141 $±$ 0.532	0.919 $±$ 0.086
√	2.860 $±$ 0.582	2.113 $±$ 0.525	0.921 $±$ 0.084

Table 6

Table 7

Ablation test results of number of regression units on WV3 Dataset （Mean ± Std）

回归单元数量	低分辨率指标			全分辨率指标
回归单元数量	SAM	ERGAS	Q	$D λ$	$D S$	QNR
1	2.885±0.590	2.139±0.528	0.921±0.083	0.014 6±0.005 9	0.027 9±0.006 8	0.958±0.010
2	2.869±0.583	2.115±0.523	0.920±0.085	0.016 0±0.006 5	0.028 9±0.002 7	0.956±0.007
3	2.860±0.582	2.113± 0.525	0.921±0.084	0.015 2±0.006 3	0.027 5±0.005 4	0.958±0.010
4	2.870±0.583	2.121±0.527	0.920±0.086	0.015 2±0.006 1	0.029 1±0.004 4	0.956±0.009
5	2.871±0.582	2.123±0.525	0.920±0.086	0.014 6±0.005 9	0.028 9±0.003 5	0.957±0.008
6	5.801±1.862	7.160±1.878	0.617±0.101	0.023 8±0.006 9	0.081 6±0.031 7	0.897±0.036

Table 7

Fig.5

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模型	模型规模/MB	FLOPs/10⁹	参数量/10⁶	推理时间/ms	推理速度/（frame∙s^-1）
DiCNN^［24］	0.18	12.231	0.047	2.13	469.39
FusionNet^［40］	0.30	78.517	0.153	3.81	262.13
LAGConv^［27］	0.58	8.270	0.054	91.06	10.98
CANNet^［44］	3.00	7.069	0.308	3 107.56	0.32
ARConv^［28］	60.74	240.432	4.620	347.60	2.88
AFAR-Net	60.83	428.117	4.988	526.49	1.90

AFAR-Net: Autoregressive and feedback-driven adaptive rectangular convolution network for pansharpening

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