Journal of Electrical and Computer Engineering

Volume 2018, Article ID 9684629, 9 pages

https://doi.org/10.1155/2018/9684629

## Extraction of Earth Surface Texture Features from Multispectral Remote Sensing Data

^{1}College of Automation, Harbin Engineering University, Harbin 150001, China^{2}China Ship Development and Design Center, Wuhan, China^{3}92730 Army, Sanya 572016, China

Correspondence should be addressed to Feng Gao; nc.ude.uebrh@91gnefoag

Received 12 December 2017; Accepted 18 March 2018; Published 25 October 2018

Academic Editor: Jose R. C. Piqueira

Copyright © 2018 Zhenxing Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Earth surface texture features referring to as visual features of homogeneity in remote sensing images are very important to understand the relationship between surface information and surrounding environment. Remote sensing data contain rich information of earth surface texture features (image gray reflecting the spatial distribution information of texture features, for instance). Here, we propose an efficient and accurate approach to extract earth surface texture features from remote sensing data, called gray level difference frequency spatial (GLDFS). The gray level difference frequency spatial approach is designed to extract multiband remote sensing data, utilizing principle component analysis conversion to compress the multispectral information, and it establishes the gray level difference frequency spatial of principle components. In the end, the texture features are extracted using the gray level difference frequency spatial. To verify the effectiveness of this approach, several experiments are conducted and indicate that it could retain the coordination relationship among multispectral remote sensing data, and compared with the traditional single-band texture analysis method that is based on gray level co-occurrence matrix, the proposed approach has higher classification precision and efficiency.

#### 1. Introduction

Remote sensing technology can extract high resolution regional marine environmental information in time, especially for the complex sea area. Multispectral remote sensing data reflects the interested target or regional radiation characteristics through the electromagnetic spectrum of multiband, and it has the advantages of wide range, multiphase, multiband, and high resolution. Remote sensing image could enrich the spectral characteristics of landmark and find out more detailed information, such as the structure, shape, and texture. However, in virtue of the fact that same objects possess different spectral and different objects share same spectral, the applications of remote sensing data would be serious restricted if only spectral information is taken into consideration. The earth surface texture is a good solution to the problem because of the stability characteristics [1].

The classical texture extraction and analytic approaches include gray level co-occurrence matrix method [2, 3], wavelet analysis method [4, 5], Gabor spectrum method [6], and so forth. While all these methods could only be applied to analyze the information of single band in remote sensing images, for multispectral remote sensing data, all the bands should be processed separately, which would decrease the extraction efficiency badly.

Because of the geometric characteristics of the surface object, it has a unique texture features on the remote sensing images. So, the different surface objects can be extracted through the texture features. This paper utilizes gray level difference frequency spatial to extract texture features of multiband remote sensing data. We firstly conduct principal component analysis (PCA) on the eight bands of Worldview-II multispectral images and compress these data on basis of guaranteeing against loss of spectral information. Make gray difference statistics on the compressed principle components and establish the gray level difference frequency spatial. In the experiments, the gray level difference frequency spatial is used to extract texture features, and a comparison with Gray Level Co-occurrence Matrix (GLCM) is made. The experimental results indicate that the gray level difference frequency spatial has higher classification accuracy and efficiency.

#### 2. Worldview-II Multispectral Remote Sensing Data

Worldview-II is one of the highest resolution remote sensing satellites, and it has the highest spatial resolution (0.46 m in the panchromatic band and 1.84 m in the multispectral bands). It provides high resolution multispectral data with eight bands, which include four conventional bands (red, green, blue, and near-infrared 1) and four characteristic bands (coastal, yellow, red edge, and near-infrared 2). The data analyzed in this paper are Worldview-II multispectral remote sensing image of the Sea Islands; the texture features of eight bands are extracted. Firstly, we calibrate the data and get the radiance data. Secondly, atmospheric correction is conducted to eliminate the influence of atmosphere and illumination, and the actual reflectance of surface objects is obtained. Finally, we make orthorectification on the data through a few control points, thus eliminating the geometric distortion.

#### 3. Compression of Multispectral Remote Sensing Data

Principal component analysis could project the high dimensional data onto a low-dimensional space. It takes the variance in size as the evaluation standard of information quantity; the greater the variance, the more information it provides [7, 8]. On the premise of keeping useful information of multispectral remote sensing data, principal component analysis could reduce the correlation and redundant information in order to compress multispectral remote sensing data. We transform Worldview-II multispectral remote sensing data into a column vector as follows:

Principal component analysis makes a combination of through linear transformation and guarantees that has the largest variance after transformation, as shown in the following equation:where is the m-dimensional space to be determined and is the covariance matrix of , thereby, the variance of could be computed as follows:

Thereby, solving the maximum value of is equal to seeking the vector that makes the largest. The length is limited to unit length, and then the question is converted to

In last equation, the covariance matrix could be expressed as follows:where is the characteristic value of and is satisfied. , where is the eigenvector corresponding to the unit orthogonal eigenvectors. Let and multiplying Equation (5) with and on the left and right side separately, we get

Let , then ; Equation (6) satisfies

Equation (4) can be rewritten as follows:

If , then , which indicates that the maximum value of is at the point of under the condition of , thereby the first PCA principle component could be expressed as . The contribution rate reflects the information quantity contained in each principle component, and the contribution rate of the *i*-th principal component could be computed as follows:

The cumulative contribution rate of the first principal components is as follows:

The contribution rate indicates the ability that principle components reflect . It determines the number of principal components after compression of multispectral remote sensing data.

#### 4. Texture Features Extraction from Multispectral Remote Sensing Data

##### 4.1. Gray Level Co-Occurrence Matrix

Gray Level Co-occurrence Matrix is the most direct and simplest texture analysis approach, which considers the spatial structure of remote sensing images [9]. It describes the image texture through the two-order combined conditional probability density among image pixels [10]. Assume the remote sensing image is of size ; the gray level is ; the distance between two pixels is ; the angle is ; the gray levels are separately and ; the times that these two pixels appear simultaneously is which could be expressed as follows:Where and represent the number of remote sensing image pixels in a row and a column; , , , , are the pixel coordinates in the image. Figure 1 shows the spatial sketch map of GLCM. If the remote sensing image has picture gray levels, the size of the gray level co-occurrence matrix is , represents the distance of two pixels in the remote sensing image, represents the angle between the connection line of the two pixels and horizontal direction, and it is usually set as , , , and . The element at the *a*-th row and *b*-th column in represents the appearance times of all the pixel couples that are apart from each other in the direction, with gray values *i* and *j*, respectively. is related to the image, the step is usually set as , and the central pixel to be operated and compared with the directly adjacent pixel.