Incorporation of Multiscale Hierarchical Features for Remote-Sensing Classification

Fig. 5. Scene instances of two datasets. (a) Airplane; (b) beach; (c) ship; (d) cloud; (e) island; (f) bridge; (g) parking; (h) center; (i) storage tank; (j) playground

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Fig. 6. Confusion matrix of AID dataset under 50% training ratio

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Fig. 7. Confusion matrix of NWPU-RESISC45 dataset under 20% training ratio

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Fig. 8. Visual CAM comparison. (a) Basketball court; (b) bridge; (c) church; (d) freeway; (e) roundabout; (f) ship; (g) parking

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Table 1. Experimental configuration
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Table 1. Experimental configuration
Parameter Value
CPU i7-12700
GPU NVIDIA GeForce RTX 3090Ti 24 GB
Language Python 3.9.12
Operating system Windows 11
Deep learning framework PyTorch 1.11.0+CUDA 11.3

Table 2. Key parameter setting
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Table 2. Key parameter setting
Parameter Value
Epochs 300
Initial learning rate 0.0002
Final learning rate 0.00001
Optimizer AdamW
Batch size 128
Warmup 20
Weight decay 0.003
Random seed 42

Table 3. OA of different models on AID dataset and NWPU-RESISC45 dataset

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Table 3. OA of different models on AID dataset and NWPU-RESISC45 dataset

Method	Parameters /10⁶	NWPU（1∶9）	NWPU（2∶8）	AID（2∶8）	AID（5∶5）
VGGNet16^［27］	138.36	76.47	79.79	86.59	89.64
GoogleNet^［27］	54.40	76.19	78.48	83.44	86.39
ResNet50^［29］	23.58	86.23	88.93	92.39	94.96
DenseNet121^［30］	7.98	88.31	90.47	93.76	94.73
ViT-B^［11］	85.63	87.59	90.87	91.16	94.44
PVT-M^［31］	43.32	90.51	92.66	92.84	95.93
DeiT-B^［32］	85.97	91.86	93.83	93.41	96.04
Swin-B^［33］	89.74	91.80	94.14	94.86	97.80
EMTCAL^［34］	–	91.63	93.65	94.69	96.41
GCSANet^［35］	8.11	93.39	94.65	95.96	97.53
MGSNet^［36］	–	92.40	94.57	95.46	97.18
EMSCNet（ViT-B）^［37］	173.64	93.58	95.37	96.02	97.35
MHFNet	41.85	94.09	95.73	97.12	98.63

Table 4. Impact of MSIT and ATM on model performance
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Table 4. Impact of MSIT and ATM on model performance
Module Parameters /10⁶ NWPU（1∶9） NWPU（2∶8） AID（2∶8） AID（5∶5）
None 31.18 92.27 94.10 95.38 96.93
ATM 32.53 92.88 94.74 96.05 97.57
ATM+MSIT 41.85 94.09 95.73 97.12 98.63

Table 5. Impact of number of MSITs in each branch on model performance

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Table 5. Impact of number of MSITs in each branch on model performance

Option	Parameters /10⁶	NWPU（1∶9）	NWPU（2∶8）	AID（2∶8）	AID（5∶5）
N=0	32.53	92.88	94.74	96.05	97.57
N=1	37.68	93.79	95.57	96.74	98.37
N=2	41.85	94.09	95.73	97.12	98.63
N=3	46.02	93.82	95.66	96.89	98.46
N=4	50.18	93.74	95.59	96.76	98.38

Table 6. Effectiveness of various components in MSIT

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Table 6. Effectiveness of various components in MSIT

Module	Parameters /10⁶	NWPU（1∶9）	NWPU（2∶8）	AID（2∶8）	AID（5∶5）
None	32.53	92.88	94.74	96.05	97.57
MSIA	38.43	93.73	95.41	96.75	98.30
MSIA+MGFN	41.76	93.97	95.65	96.94	98.52
MSIA+MGFN+LRC	41.85	94.09	95.73	97.12	98.63

Table 7. Impact of different feature extractors on model performance

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Table 7. Impact of different feature extractors on model performance

Method	Parameters /10⁶	FLOPs /G	NWPU（1∶9）	NWPU（2∶8）	AID（2∶8）	AID（5∶5）
MHFNet（ResNet50）	34.61	4.71	92.63	94.58	95.39	96.94
MHFNet（ViT-B）	96.17	56.30	93.28	94.83	95.21	96.47
MHFNet（Swin-B）	102.38	16.09	93.74	95.80	96.83	98.92
MHFNet（FasterNet-S）	41.85	5.12	94.09	95.73	97.12	98.63

Table 8. OA, precision, recall rate, and F1 score for each category in AID dataset

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Table 8. OA, precision, recall rate, and F1 score for each category in AID dataset

Category	OA	Precision	Recall	F1 score	Category	OA	Precision	Recall	F1 score
1	99.63	99.26	100.00	99.63	16	100.00	100.00	99.55	99.77
2	99.12	100.00	100.00	100.00	17	95.04	95.93	94.19	95.05
3	100.00	100.00	100.00	100.00	18	100.00	100.00	100.00	100.00
4	98.81	99.78	99.38	99.58	19	99.83	99.04	98.59	98.81
5	99.44	99.00	99.50	99.25	20	99.45	99.39	98.22	98.80
6	99.47	99.22	90.81	94.83	21	99.61	100.00	100.00	100.00
7	97.22	97.67	100.00	98.82	22	99.80	100.00	100.00	100.00
8	97.08	97.61	98.37	97.99	23	96.33	96.35	95.60	95.97
9	97.87	97.23	100.00	98.60	24	98.09	98.78	98.11	98.44
10	97.94	98.78	100.00	99.39	25	96.91	97.72	97.83	97.77
11	98.91	98.72	100.00	99.36	26	97.32	99.59	98.65	99.12
12	99.08	100.00	100.00	100.00	27	95.15	95.25	99.60	97.38
13	98.52	99.65	99.41	99.53	28	99.44	99.16	96.19	97.65
14	97.66	98.15	100.00	99.07	29	100.00	100.00	99.01	99.50
15	99.18	99.26	100.00	99.63	30	99.28	100.00	100.00	100.00

Table 9. OA, precision, recall rate, and F1 score for each category in NWPU-RESISC45 dataset

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Table 9. OA, precision, recall rate, and F1 score for each category in NWPU-RESISC45 dataset

Category	OA	Precision	Recall	F1 score	Category	OA	Precision	Recall	F1 score
1	98.67	99.47	100.00	99.73	24	91.96	91.57	88.27	89.89
2	97.60	98.75	98.16	98.45	25	97.38	98.16	100.00	99.07
3	94.14	94.56	94.93	94.74	26	97.01	98.93	96.35	97.62
4	98.88	99.15	91.62	95.24	27	96.87	97.42	94.12	95.74
5	98.45	99.43	96.17	97.77	28	90.71	90.91	84.75	87.72
6	97.57	98.16	97.33	97.74	29	95.48	96.88	100.00	98.42
7	100.00	100.00	99.81	99.90	30	96.93	96.14	92.13	94.09
8	86.48	86.92	88.05	87.48	31	93.26	93.68	93.69	93.68
9	98.55	98.19	100.00	99.09	32	96.79	98.76	93.47	96.04
10	98.81	98.45	99.27	98.86	33	96.17	96.81	93.19	94.97
11	89.90	89.98	96.76	93.25	34	97.52	98.06	94.13	96.05
12	93.19	93.12	92.11	92.61	35	97.58	98.03	97.56	97.79
13	91.30	91.22	98.65	94.79	36	100.00	100.00	100.00	100.00
14	97.27	98.15	98.70	98.42	37	96.15	98.56	97.31	97.93
15	96.31	97.07	90.62	93.73	38	99.22	100.00	99.23	99.61
16	99.23	100.00	100.00	100.00	39	95.57	96.27	97.56	96.91
17	85.18	85.50	97.29	91.01	40	93.40	94.90	98.22	96.53
18	99.63	100.00	100.00	100.00	41	95.70	96.52	100.00	98.23
19	93.41	93.19	93.86	93.52	42	86.51	86.44	87.74	87.09
20	92.49	92.26	92.81	92.53	43	93.97	93.20	98.08	95.58
21	93.57	93.04	97.32	95.13	44	98.19	99.65	97.16	98.39
22	91.40	91.29	95.85	93.51	45	97.45	98.47	89.91	94.00
23	96.13	97.50	95.96	96.72

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Fu Lü, Yuxuan Xie, Yongan Feng. Incorporation of Multiscale Hierarchical Features for Remote-Sensing Classification[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0228004

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