Application of Image Filtering Operator in Extraction of Soil Salinization Information

Zheng Wang; Fei Zhang; Xianlong Zhang; Yishan Wang

doi:10.3788/LOP57.042801

Laser & Optoelectronics Progress, Volume. 57, Issue 4, 042801(2020)

Application of Image Filtering Operator in Extraction of Soil Salinization Information

Zheng Wang^1,2, Fei Zhang^1,2,3、*, Xianlong Zhang^1,2, and Yishan Wang^1,2

¹Key Laboratory of Smart City and Environmental Modeling of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China

²Key Laboratory of Oasis Ecology, Urumqi, Xinjiang 830046, China

³Engineering research center of Central Asia Geoinformation development and utilization, National administration of surveying, Mapping and Geoinformation, Urumqi, Xinjiang 830046, China

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Figures & Tables(10)

Fig. 1. Schematic of research area. (a) Ebinur Lake Wetland Reserve and distribution of sampling points; (b) salinized soil in Ebinur Lake Wetland Reserve; (c) salt crystals on surface of ponds in Ebinur Lake Wetland Reserve; (d) vegetation in Ebinur Lake Wetland Reserve

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Fig. 2. Matic map of salinization degree of sampling points

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Fig. 3. Schematic of classification process of SVMs

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Fig. 4. Images processed by different filtering methods. (a) Raw remote-sensing image; (b) Laplacian filtering; (c) high-pass filtering; (d) low-pass filtering; (e) Gaussian high-pass filtering; (f) Gaussian low-pass filtering; (g) median filtering; (h) directional filtering

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Fig. 5. Variation in brightness value at different bands

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Fig. 6. Classification of remote-sensing images based on different filtering methods. (a) Raw remote-sensing image; (b) Laplacian filtering; (c) high-pass filtering; (d) low-pass filtering; (e) Gaussian high-pass filtering; (f) Gaussian low-pass filtering; (g) median filtering; (h) directional filtering

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Table 1. Classification of degree of soil salinization

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Table 1. Classification of degree of soil salinization

Degree ofsoil salinization	Soil saltcontent /(g·kg^-1)	Numberof samples	Growth condition
Non-saline soil	<1	10	Healthy growth of vegetation
Mildly saline soil	1--6	15	Plant coverage is approximately 15% to 30%,and salt-sensitive vegetation may be affected
Moderately saline soil	6--10	6	Plant coverage isapproximately 10% to 15%,and salt-tolerant crops are less affected
Severely saline soil	10--20	6	Plant coverage is approximately 5% to 10%,and salt-tolerant crops and their yields are greatly affected
Saline soil	>20	1	There is only a small amount of salt-tolerantvegetation such as Haloxylon ammodendron

Table 2. Filtering matrix functions

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Table 2. Filtering matrix functions

Filtering	Filtering matrix
Laplacian	$\begin{matrix} \begin{matrix} 0 & - 1 & 0 \\ - 1 & 4 & - 1 \\ 0 & - 1 & 0 \end{matrix} \end{matrix}$
High-pass	$\begin{matrix} \begin{matrix} - 1 & - 1 & - 1 \\ - 1 & 8 & - 1 \\ - 1 & - 1 & - 1 \end{matrix} \end{matrix}$
Low-pass	$\begin{matrix} \begin{matrix} f (i - 1, j + 1) & f (i, j + 1) & f (i + 1, j + 1) \\ f (i - 1, j) & f (i, j) & f (i + 1, j) \\ f (i - 1, j - 1) & f (i, j - 1) & f (i + 1, j - 1) \end{matrix} \end{matrix}$
Gaussianhigh-pass	$\begin{matrix} \begin{matrix} - 0.0007 & - 0.0256 & - 0.0007 \\ - 0.0256 & 0.1025 & - 0.0256 \\ - 0.0007 & - 0.0256 & - 0.0007 \end{matrix} \end{matrix}$
Gaussianlow-pass	$\begin{matrix} \begin{matrix} 0.0007 & 0.0256 & 0.0007 \\ 0.0256 & 0.8948 & 0.0256 \\ 0.0007 & 0.0256 & 0.0007 \end{matrix} \end{matrix}$
Median	$\begin{matrix} \begin{matrix} f (i - 1, j + 1) & f (i, j + 1) & f (i + 1, j + 1) \\ f (i - 1, j) & f (i, j) & f (i + 1, j) \\ f (i - 1, j - 1) & f (i, j - 1) & f (i + 1, j - 1) \end{matrix} \end{matrix}$
Directional	$\begin{matrix} \begin{matrix} 0 & - 1 & 1 \\ - 1 & 0 & - 1 \\ 1 & - 1 & 0 \end{matrix} \end{matrix}$

Table 3. Classification scheme of remote-sensing image of research area

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Table 3. Classification scheme of remote-sensing image of research area

Classificationtype	Soil saltcontent /(g·kg^-1)	Description
Water body	-	The color of the water on thefalse color image is blue or black,including rivers, ditches, lakes, etc.
Non-saline soil	<1	The soil has low salt content andlow image reflectance, includingrocks, wasteland, mountains, etc.
Mildly saline soil	1--6	The soil has less salt content, and thevegetation coverage is about 8%~15%The white patches in the middle of thevegetation and the bright spot area are small
Moderately saline soil	6--10	The soil has a general salt content,vegetation coverage is about 1% to 8%, andthere are fewer white patches on the image
Severely saline soil	10--20	The soil has a high salt contentand is a heavily salinizedarea in the Ebinur Lake region
Saline soil	>20	The soil has high salt content, high spectralreflectance, obvious salt crust on the surface,white plaque distribution on the image,and basically no vegetation growth

Table 4. Assessment methods of classification accuracy of SVM

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Table 4. Assessment methods of classification accuracy of SVM

Filtering	Overallaccuracy /%	Kappacoefficient /%
Raw remotesensing image	86.7285	82.21
High-pass	87.0441	82.65
Low-pass	89.6555	86.15
Laplacian	88.6544	84.80
Directional	88.8714	85.10
Gaussian high-pass	89.6950	86.20
Gaussian low-pass	89.6087	86.58
Median	89.6678	86.16

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Zheng Wang, Fei Zhang, Xianlong Zhang, Yishan Wang. Application of Image Filtering Operator in Extraction of Soil Salinization Information[J]. Laser & Optoelectronics Progress, 2020, 57(4): 042801

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Paper Information

Category: Remote Sensing and Sensors

Received: Jul. 15, 2019

Accepted: Jul. 29, 2019

Published Online: Feb. 20, 2020

The Author Email: Fei Zhang (zhangfei3s@163.com)

DOI:10.3788/LOP57.042801

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology