Hyperspectral On-Board Classification Algorithm Based on Multiscale Feature Extraction

Fig. 2. Classification maps of each algorithm on Indian Pines dataset. (a) Ground truth; (b) SVM; (c) RF; (d) CNN1D; (e) CNN2D; (f) HybridSN; (g) A²S²K-ResNet; (h) LBP-RF; (i) EMP-RF; (j) MF-RF

Download full size

Fig. 3. Classification maps of each algorithm on Pavia University dataset. (a) Ground truth; (b) SVM; (c) RF; (d) CNN1D; (e) CNN2D; (f) HybridSN; (g) A²S²K-ResNet; (h) LBP-RF; (i) EMP-RF; (j) MF-RF

Download full size

Fig. 4. Number of operations in the classification process of each algorithm. (a) Spectral algorithms; (b) one-stage spatial-spectral algorithms; (c) two-stage spatial-spectral algorithms

Download full size

Fig. 5. Energy consumption of each algorithm in the classification process. (a) Spectral algorithms; (b) one-stage spatial-spectral algorithms; (c) two-stage spatial-spectral algorithms

Download full size

Table 1. Statistics of operation type and number of operations of different algorithms

View table

Table 1. Statistics of operation type and number of operations of different algorithms

Algorithm		Number of operations
Algorithm		Exp	Mul	Add	Cmp
Spectral algorithm（k：number of features，l：number of classes）	SVM	M	kM+2M	kM+l
	SVM	M：number of support vectors
	RF			N	DN
	RF	$D$ ：average depth of trees， $N$ ：number of trees
	CNN1D		$(k - q + 1) q l$	$(k - q + 1) q l$
	CNN1D	$q$ ：number of values in each filter
One-stage spatial-spectral algorithm （K：kernel size， C_in：number of input channels， C_out：number of output channels）	Each convolution layer		$K^{2} \times C_{i n} \times C_{o u t}$	$K^{2} \times C_{i n} \times C_{o u t}$
Two-stage spatial-spectral algorithm（ $k^{'}$ ：number of features）	LBP-RF			$N$	$k^{'} p + D N$
	LBP-RF	$p$ ：number of points in LBP， $D / N$ ：same as RF
	EMP-RF		$M u l (P C A)$	$A d d (P C A) + N$	$\sum_{1}^{m} {S_{i}}^{2} + D N$
	EMP-RF	$M u l (P C A) 、 A d d (P C A)$ ：number of Mul/Add in PCA， $S_{i}$ ：size of each structuring element， $m$ ：number of structuring elements， $D / N$ ：same as RF
	MF-RF			$N$	${(2 r_{m a x} + 1)}^{2} + D N$
	MF-RF	$r_{m a x}$ ：radius of the largest filter， $D / N$ ：same as RF

Table 2. Classification results on Indian Pines dataset

View table

Table 2. Classification results on Indian Pines dataset

Class	Spectral algorithm			One-stage spatial-spectral algorithm			Two-stage spatial-spectral algorithm
Class	SVM	RF	CNN1D	CNN2D	HybridSN	A²S²K-ResNet	LBP-RF	EMP-RF	MF-RF
Alfalfa	81.82	100.00	87.50	100.00	80.49	100.00	95.24	97.83	91.84
Corn-notil	78.91	73.58	73.38	90.24	95.10	97.78	82.34	94.76	95.49
Corn-mintill	80.69	75.93	75.30	90.85	99.46	98.69	88.00	95.17	94.72
Corn	63.14	59.60	79.31	87.98	93.90	96.27	74.05	83.87	93.81
Grass-pasture	92.83	88.70	91.39	93.95	98.16	99.31	97.17	94.00	96.21
Grass-trees	85.24	82.98	94.59	93.98	99.24	99.44	88.18	97.90	95.45
Grass-pasture-mowed	83.87	100.00	95.00	100.00	100.00	92.22	90.00	100.00	89.66
Hay-windrowed	92.59	86.34	93.25	98.73	99.53	99.32	94.43	100.00	97.95
Oats	100.00	100.00	100.00	90.91	61.11	84.65	100.00	100.00	100.00
Soybean-notill	80.05	72.25	82.65	91.12	98.17	97.56	92.16	92.94	95.96
Soybean-mintill	77.97	73.37	69.97	94.80	99.05	99.13	86.45	94.68	96.16
Soybean-clean	77.26	66.60	85.78	92.58	90.26	98.10	87.74	89.64	92.95
Wheat	90.13	90.91	94.34	95.71	95.14	99.20	100.00	100.00	98.51
Woods	93.25	93.06	93.31	96.48	99.47	99.29	99.37	99.21	99.61
Buildings-Grass-Trees-Drives	73.82	58.43	71.70	96.21	95.97	98.00	97.49	94.33	98.21
Stone-Steel-Towers	98.77	98.75	100.00	78.38	90.48	96.30	92.86	97.89	98.78
OA	82.34	77.62	80.11	93.43	97.44	98.57	89.66	95.15	96.26
AA	84.39	82.53	86.72	93.24	93.47	97.20	91.59	95.76	95.96
Kappa	79.75	74.26	77.01	92.50	97.08	98.37	88.14	94.46	95.73

Table 3. Classification results on Pavia University dataset

View table

Table 3. Classification results on Pavia University dataset

Class	Spectral algorithm			One-stage spatial-spectral algorithm			Two-stage spatial-spectral algorithm
Class	SVM	RF	CNN1D	CNN2D	HybridSN	A²S²K-ResNet	LBP-RF	EMP-RF	MF-RF
Asphalt	93.60	92.03	95.15	98.75	100.00	99.76	97.12	99.40	98.68
Meadows	96.17	90.22	98.17	99.33	100.00	99.95	98.65	99.82	99.30
Gravel	88.38	86.50	89.82	97.90	98.68	99.42	96.69	99.86	98.76
Trees	96.25	96.21	95.10	96.19	99.45	99.88	97.10	99.90	99.14
Painted metal sheets	99.04	98.23	99.63	99.56	100.00	99.97	99.48	100.00	99.85
Bare Soil	94.65	92.93	91.94	99.26	100.00	99.96	98.26	99.94	99.80
Bitumen	92.73	86.11	92.05	92.81	100.00	100.00	99.13	99.70	99.62
Self-Blocking Bricks	86.00	82.08	85.33	97.48	99.70	99.00	93.86	99.08	99.17
Shadows	100.00	100.00	99.37	99.55	94.60	100.00	99.79	99.68	99.79
OA	94.41	90.56	95.07	98.58	99.72	99.81	97.81	99.71	99.25
AA	94.09	91.59	94.06	97.87	99.11	99.77	97.79	99.71	99.35
Kappa	92.56	87.26	93.48	98.12	99.63	99.75	97.10	99.62	99.01

Table 4. Classification times of different algorithms on two datasets

View table

Table 4. Classification times of different algorithms on two datasets

Dataset

Spectral algorithm

One-stage spatial-spectral algorithm

Two-stage spatial-spectral algorithm

SVM

CNN1D

CNN2D

HybridSN

A²S²K-

ResNet

LBP-RF

EMP-RF

MF-RF

Indian Pines

3.41

0.54

1.20

6.11

72.30

45.68

4.38

1.48

1.07

Pavia University

39.23

4.15

7.43

59.56

277.31

117.42

79.70

12.69

16.69

Table 5. Classification results on HyRANK dataset

View table

Table 5. Classification results on HyRANK dataset

Class	RF	L1+RF	ST+RF	ST+CORAL+RF	MF-RF（17）	ST+CORAL+MF-RF（52）
Dense Urban Fabric	5.78	29.17	4.39	3.70	8.21	15.49
Mineral Extraction Sites	0.00	17.07	16.24	34.09	2.50	33.33
Non-Irrigated Arable Land	25.00	83.14	45.71	92.18	66.67	76.61
Fruit Trees	0.00	0.00	1.52	3.05	0.00	1.96
Olive Groves	2.22	44.44	0.00	81.40	0.00	62.63
Coniferous Forest	100.00	87.50	64.60	39.49	85.71	28.15
Dense Sclerophyllous Vegetation	67.54	67.88	68.45	71.68	66.59	69.38
Sparce Sclerophyllous Vegetation	44.33	37.46	51.43	49.70	45.65	51.60
Sparcely Vegetated Areas	9.38	11.66	4.99	17.28	9.29	17.92
Rocks and Sand	9.45	28.00	56.91	57.32	8.93	58.40
Water	62.28	95.15	100.00	100.00	67.64	100.00
Coastal Water	3.01	100.00	100.00	99.34	2.15	96.16
OA	46.66	51.99	55.45	58.59	48.57	60.03
AA	27.42	50.12	42.85	54.10	30.28	50.97
Kappa	33.13	41.27	45.95	49.92	35.52	50.67

Table 6. Classification results of RF, CNN1D, and MF-RFs based on different feature values

View table

Table 6. Classification results of RF, CNN1D, and MF-RFs based on different feature values

Dataset	PSNR /dB	RF	CNN1D	MF-RF（minimum）	MF-RF（maximum）
Indian Pines		77.62	80.11	93.76	96.26
	36.38	77.10	80.07	93.27	95.07
	26.38	74.14	76.19	93.04	94.67
Pavia University		90.56	95.07	99.06	99.25
	35.07	89.94	93.61	98.80	99.28
	25.07	85.79	87.77	97.94	99.31

Table 7. Comparison of MF and different classifiers combinations
View table
Table 7. Comparison of MF and different classifiers combinations
Dataset MF-SVM MF-FC MF-RF
Indian Pines OA 94.89 79.61 96.26
AA 95.60 76.72 95.96
Kappa 94.17 76.62 95.73
Pavia University OA 99.47 90.46 99.25
AA 99.49 89.13 99.35
Kappa 99.29 87.33 99.01

Table 8. Comparison of different CNN feature extraction methods and RF combinations
View table
Table 8. Comparison of different CNN feature extraction methods and RF combinations
Dataset CNN1D-RF CNN2D-RF MF-RF
Indian Pines OA 80.88 92.53 96.26
AA 87.78 93.65 95.96
Kappa 77.93 91.44 95.73
Pavia
University
OA 95.37 97.93 99.25
AA 95.19 97.79 99.35
Kappa 93.83 97.25 99.01

Table 9. Energy consumption of different operation types
View table
Table 9. Energy consumption of different operation types
Operation type Data precision /bit Energy cost /pJ
Integer comparison 8 0.008
Floating-point addition 32 0.9
Floating-point multiplication 32 3.7
Floating-point exponentiation 32 38.975

Table 10. Energy consumption of each algorithm in classification process

View table

Table 10. Energy consumption of each algorithm in classification process

Classification algorithm	Indian Pines	Pavia University
SVM	2.8×10⁷	2.4×10⁷
RF	1.0×10²	1.1×10²
CNN1D	7.2×10⁵	3.1×10⁵
CNN2D	1.8×10⁸	1.3×10⁸
HybridSN	1.1×10⁹	1.1×10⁹
A²S²K-ResNet	7.7×10¹⁰	3.8×10¹⁰
LBP-RF	1.1×10²	1.3×10²
EMP-RF	5.4×10⁷	1.8×10⁶
MF-RF	2.7×10²	1.0×10³

Tools

Get Citation

Copy Citation Text

Shuai Yuan, Yanan Sun, Weifeng He, Shikui Tu. Hyperspectral On-Board Classification Algorithm Based on Multiscale Feature Extraction[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010004

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Image Processing

Received: Dec. 20, 2021

Accepted: Feb. 8, 2022

Published Online: May. 17, 2023

The Author Email: Yanan Sun (sunyanan@sjtu.edu.cn)

DOI:10.3788/LOP213289

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Statistics of operation type and number of operations of different algorithms

Table 1. Statistics of operation type and number of operations of different algorithms

Table 2. Classification results on Indian Pines dataset

Table 2. Classification results on Indian Pines dataset

Table 3. Classification results on Pavia University dataset

Table 3. Classification results on Pavia University dataset

Table 4. Classification times of different algorithms on two datasets

Table 4. Classification times of different algorithms on two datasets

Table 5. Classification results on HyRANK dataset

Table 5. Classification results on HyRANK dataset

Table 6. Classification results of RF, CNN1D, and MF-RFs based on different feature values

Table 6. Classification results of RF, CNN1D, and MF-RFs based on different feature values

Table 7. Comparison of MF and different classifiers combinations

Table 7. Comparison of MF and different classifiers combinations

Table 8. Comparison of different CNN feature extraction methods and RF combinations

Table 8. Comparison of different CNN feature extraction methods and RF combinations

Table 9. Energy consumption of different operation types

Table 9. Energy consumption of different operation types

Table 10. Energy consumption of each algorithm in classification process

Table 10. Energy consumption of each algorithm in classification process