Extraction of Earth Surface Texture Features from Multispectral Remote Sensing Data

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.

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

“…Texture analysis is used to represent the degree of coarseness or fineness of objects within digital image samples. is type of analysis has been widely used in various fields of study including civil engineering [51], remote sensing [52], biomedical imaging [53], and industrial engineering [54,55]. Based on the collected image samples, meaningful features that represent the texture properties of image regions can be computed and used for object classification [34].…”

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF

“…Modeling strategy II adds 25 texture features to the single-band variables, and the results show that modeling strategy II improves R 2 by 0.0129 and RPD by 0.8118.The overall improvement of R 2 is not significant, which may be due to the fact that texture features are also statistics of single-band information ( Zhang et al., 2018 ), and although texture features increase the spatial distribution describing spatial dimensionality information, it lacks the characterization of interconnected information between bands ( Fu Y. et al., 2021 ). The RPD enhancement is more obvious compared to R 2 , and the stability of the model is significantly improved by adding texture features.…”