Classification of hyperspectral images using fusion of CNN and MiniGCN with SVM

Figures & data

Figure 1. Convolution process (3 × 3 convolutional operator).

Figure 2. Graph structure.

Figure 3. (a) Full graph (b) MiniGCN.

Figure 4. Proposed Framework of FusionNet.

Table 1. FusionNet - layer-wise network architecture.

Layer	CNN	MiniGCN
Stage 1	3 × 3 Conv	Batch Normalization
	Batch Normalization	GCN
	2 × 2 AvgPool	Batch Normalization
	ReLU	ReLU
Output size	4 × 4 × 32	128
Stage 2	3 × 3 Conv	-
	Batch Normalization
	2 × 2 AvgPool
	ReLU
Output size	4 × 4 × 64	-
Stage 3	3 × 3 Conv	-
	Batch Normalization
	2 × 2 AvgPool
	ReLU
Output size	1 × 1 × 128	-
Fully connected	Fully Connected	-
	Batch Normalization
	ReLU
Output size	128	-
Fusion	Fully Connected
	Batch Normalization
	ReLU
Output size	256
Output	Fully Connected
	SVM
Output size	Classes

Table 2. Implementation details.

Parameter Name	Description	Parameter Name	Description
Maximum Epochs	200	Learner rate	0.0001
Batch Size	32	Activation Function	ReLU
Optimizer	Adam	L2 regularization	0.001
Momentum	0.9	Shuffle	True
Test size	0.2	K-fold	5

Table 3. Results obtained from Indian Pines dataset.

No	Class Name	Accuracy	No	Class Name	Accuracy
1	Corn Notill	79.55	9	Soybean Clean	85.74
2	Corn Mintill	89.59	10	Wheat	97.49
3	Corn	96.47	11	Woods	98.55
4	Grass Pasture	97.66	12	Buildings Grass Trees Drives	92.43
5	Grass Trees	96.69	13	Stone Steel Towers	99.00
6	Hay Windrowed	99.54	14	Alfalfa	96.97
7	Soybean Notill	86.84	15	Grass Pasture Mowed	98.00
8	Soybean Mintill	78.81	16	Oats	91.53
Average Accuracy		92.80
Kappa		92.09

Table 4. Results obtained from Pavia University dataset.

No	Class Name	Accuracy	No	Class Name	Accuracy
1	Asphalt	97.86	6	Bare Soil	73.73
2	Meadows	96.61	7	Bitumen	96.69
3	Gravel	85.59	8	Bricks	99.65
4	Trees	90.00	9	Shadows	93.54
5	Metal Sheets	77.37	-	-	-
Average Accuracy		90.11
Kappa		89.88

Figure 5. Percentage of Training sample (80%, 70%, 60%) using Indian Pines dataset.

Table 5. Results obtained from Pavia University dataset.

Reference	Methodology	Dataset	Average Accuracy
(Sawant and Manoharan 2020)	3D-DCT + SVM	Indian Pines	84.61%
(Zhang et al. 2021)	SSOGCN		92.06%
(Wang, Liu, and Mou 2021)	MSFF + SVM		86.12%
(Anand, Veni, and Aravinth 2021)	3D-DWT + SVM		89.6%
Proposed	FusionNet + SVM		92.80%

Table 6. Class Level Comparisons.

Class Labels	Proposed (FusionNet + SVM)	3D-DCT + SVM	SSOGCN	MSFF + SVM	3D-DWT + SVM
Corn Notill	79.55	78.98	87.52	92.64	93.1
Corn Mintill	89.59	75.60	93.46	82.40	94.5
Corn	96.47	66.67	100.0	74.95	96.6
Grass Pasture	97.66	89.64	98.38	96.61	97.3
Grass Trees	96.69	97.09	98.68	98.27	98.6
Hay Windrowed	99.54	98.69	100	99.85	96.9
Soybean Notill	86.84	81.60	92.41	76.58	91.3
Soybean Mintill	78.81	82.29	90.19	94.65	93.0
Soybean Clean	85.74	74.47	95.76	89.37	96.3
Wheat	97.49	92.07	99.35	97.87	96.4
Woods	98.55	96.44	97.12	98.92	92.0
Buildings Grass Trees Drives	92.43	65.58	99.11	79.13	91.2
Stone Steel Towers	99.00	94.59	100.0	88.54	84.6
Alfalfa	96.97	69.44	20.95	96.03	93.3
Grass Pasture Mowed	98.00	81.82	100	68.49	100
Oats	91.53	56.12	100	76.34	100

Sawant, Shrutika S., and Prabukumar Manoharan. 2020. “Unsupervised Band Selection Based on Weighted Information Entropy and 3D Discrete Cosine Transform for Hyperspectral Image Classification.” International Journal of Remote Sensing 41 (10), doi:10.1080/01431161.2019.1711242.

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Zhang, Minghua, Hongling Luo, Wei Song, Haibin Mei, and Cheng Su. 2021. “Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification.” Remote Sensing 13 (21): 4342. doi:10.3390/rs13214342.

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Wang, Wenning, Xuebin Liu, and Xuanqin Mou. 2021. “Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification.” Remote Sensing 13 (4), doi:10.3390/rs13040547.

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Anand, R., S. Veni, and J. Aravinth. 2021. “Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform.” Remote Sensing 13 (7), doi:10.3390/rs13071255.

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Data availability statement

We have utilized publicly available datasets: 1. The Indian Pines dataset is accessible at doi:10.4231/R7RX991C. 2. The Pavia University dataset can be found at https://avng.jpl.nasa.gov/pub/ABoselli/f970619t01p02_r02c/.