Hyperspectral Remote Sensing Image Cloud Detection Based on Spectral Analysis and Dynamic Fractal Dimension

Dongyu Xu; Xiaorun Li; Liaoying Zhao; Rui Shu; Qijia Tang

doi:10.3788/LOP56.101003

Laser & Optoelectronics Progress, Volume. 56, Issue 10, 101003(2019)

Hyperspectral Remote Sensing Image Cloud Detection Based on Spectral Analysis and Dynamic Fractal Dimension

Dongyu Xu¹, Xiaorun Li^1、*, Liaoying Zhao², Rui Shu³, and Qijia Tang³

Author Affiliations

¹ School of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China

² Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China

³ Shanghai Institute of Satellite Engineering, Shanghai 200240, China

show less

Abstract Get PDF(in Chinese)

Figures & Tables(12)

Fig. 1. Remote sensing image with thick clouds, thin clouds, snow, sea, and land and their spectral curves. (a) Remote sensing image; (b) spectral curves

Download full size

$Flow chart of cloud detection algorithm based on dynamic fractal dimension and radiation characteristics$

Fig. 2. Flow chart of cloud detection algorithm based on dynamic fractal dimension and radiation characteristics

Download full size

Fig. 3. Sample images of remote sensing cloud detection. (a) Sample A; (b) sample B; (c) sample C; (d) sample D; (e) sample E; (f) sample F; (g) sample G; (h) sample H

Download full size

Fig. 4. True value cloud maps. (a) Sample A; (b) sample B; (c) sample C; (d) sample D; (e) sample E; (f) sample F; (g) sample G; (h) sample H

Download full size

Fig. 5. Detection results of MRC algorithm. (a) Sample A; (b) sample B; (c) sample C; (d) sample D; (e) sample E; (f) sample F; (g) sample G; (h) sample H

Download full size

Fig. 6. Detection results of DFD_RC algorithm. (a) Sample A; (b) sample B; (c) sample C; (d) sample D; (e) sample E; (f) sample F; (g) sample G; (h) sample H

Download full size

Fig. 7. Partially enlarged images of MRC algorithm. (a) Sample B; (b) sample H; (c) sample G; (d) sample C

Download full size

Fig. 8. Partially enlarged images of DFD_RC algorithm. (a) Sample B; (b) sample H; (c) sample G; (d) sample C

Download full size

Table 1. Information of remote sensing cloud detection sample images

View table

Table 1. Information of remote sensing cloud detection sample images

Sample number	Imaging time	Location	Type of land	Type of cloud
Sample A	2017-02-03	Hong Kong	Sea	Thick cloud (small quantity)
Sample B	2016-09-06	Japan Island	Sea	Thick cloud (medium quantity)
Sample C	2017-04-23	Caribbean Sea	Sea	Thick cloud (large quantity)
Sample D	2017-02-03	Hong Kong	Sea, Land	Thin cloud (small quantity)
Sample E	2017-01-17	Norway	Sea, Land, Snow	Thick and thin cloud (small quantity)
Sample F	2017-04-23	Caribbean Sea	Sea, land	Thick and thin cloud (small quantity)
Sample G	2017-11-30	New York	Land	Thin cloud (large quantity)
Sample H	2017-11-30	New York	Land	Thick cloud (small quantity)

Table 2. Cloud content ratios of true value cloud maps %

View table

Table 2. Cloud content ratios of true value cloud maps %

Parameter	Sample A	Sample B	Sample C	Sample D	Sample E	Sample F	Sample G	Sample H
Cloud ratio	11.6415	30.9074	69.3540	37.0145	13.4696	10.0031	43.5738	7.0778
Thick cloud ratio	7.8491	22.6727	50.5277	7.3675	7.2262	7.1581	4.7550	5.9434
Thin cloud ratio	3.7924	8.2347	18.8264	29.6471	6.2434	2.8449	38.8188	1.1343

Table 3. Detection results of cloud content%

View table

Table 3. Detection results of cloud content%

Parameter	Sample A	Sample B	Sample C	Sample D	Sample E	Sample F	Sample G	Sample H
Cloud ratio byMRC	12.2374	24.1508	28.4241	1.8609	20.8452	7.3786	2.6832	0.6023
Thick cloud ratioby MRC	5.8172	10.4733	1.0546	0.3762	9.3589	6.1693	2.6832	0.6023
Thin cloud ratioby MRC	6.4202	13.6775	27.3695	1.4847	11.4863	1.2093	0	0
Cloud ratio byDFD_RC	11.5851	28.2615	64.7071	13.0741	13.0026	9.7690	38.4335	6.7717
Thick cloud ratioby DFD_RC	7.9308	19.5402	52.0699	7.8912	7.5631	9.0374	7.9997	6.1051
Thin cloud ratioby DFD_RC	3.6542	8.7213	12.6372	5.1829	5.4395	0.7316	30.4338	0.6667

Table 4. Classification indexes

View table

Table 4. Classification indexes

Parameter	Sample A	Sample B	Sample C	Sample D	Sample E	Sample F	Sample G	Sample H
Recall rate by MRC /%	95.5	90.3	54.7	8.5	86.4	88.1	8.7	10.5
Leak alarm by MRC /%	4.5	9.7	45.3	91.5	13.6	11.9	91.3	89.5
Accuracy rate by MRC /%	97.2	96.5	99.6	97.8	92.4	96.3	99.1	98.7
False alarm by MRC /%	2.8	3.5	0.4	2.2	7.6	3.7	0.9	1.3
Recall rate by DFD_RC /%	97.7	97.2	98.6	74.3	93.8	95.5	90.0	91.1
Leak alarm by DFD_RC /%	2.3	2.8	1.4	25.7	6.2	4.5	10.0	8.9
Accuracy rate by DFD_RC /%	98.8	96.3	99.6	98.7	87.3	96.1	96.7	96.5
False alarm by DFD_RC /%	1.2	3.7	0.4	1.3	12.7	3.9	3.3	3.5
Mean time by MRC /s	0.3807
Mean time by DFD_RC /s	0.7900

Tools

Get Citation

Copy Citation Text

Dongyu Xu, Xiaorun Li, Liaoying Zhao, Rui Shu, Qijia Tang. Hyperspectral Remote Sensing Image Cloud Detection Based on Spectral Analysis and Dynamic Fractal Dimension[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101003

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Image Processing

Received: Oct. 23, 2018

Accepted: Dec. 13, 2018

Published Online: Jul. 4, 2019

The Author Email: Xiaorun Li (lxr@zju.edu.cn)

DOI:10.3788/LOP56.101003

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology