Keller, J. M., Chen, S., & Crownover, R. M. (1989). Texture Description and Segmentation through Fractal Geometry. Computer Vision, Graphics, and Image Processing, 45, 150-166.
https://doi.org/10.1016/0734-189X(89)90130-8

has been cited by the following article:

• TITLE: Research on Extraction Method of Surface Information Based on Multi-Feature Combination Such as Fractal Texture

  AUTHORS: Zhen Chen, Yiyang Zheng

  KEYWORDS: Complex Surface Remote Sensing Information Extraction, Remote Sensing Land Classification, Transfer Learning, Brownian Motion Fractal Texture

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.11 No.10, October 19, 2023

  ABSTRACT: Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types; 2) Undulating terrains; 3) Small fragmented land; 4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion; 2) extraction research on color features; 3) extraction research on vegetation index; 4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.