TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 2: Line Segments Linking and Merging

Mehdi Rahnama,Richard Gloaguen

doi:10.3390/rs61111468

Abstract

Extraction and interpretation of tectonic lineaments is one of the routines for mapping large areas using remote sensing data. However, this is a subjective and time-consuming process. It is difficult to choose an optimal lineament extraction method in order to reduce subjectivity and obtain vectors similar to what an analyst would manually extract. The objective of this study is the implementation, evaluation and comparison of Hough transform, segment merging and polynomial fitting methods towards automated tectonic lineament mapping. For this purpose we developed a new MATLAB-based toolbox (TecLines). The proposed toolbox capabilities were validated using a synthetic Digital Elevation Model (DEM) and tested along in the Andarab fault zone (Afghanistan) where specific fault structures are known. In this study, we used filters in both frequency and spatial domains and the tensor voting framework to produce binary edge maps. We used the Hough transform to extract linear image discontinuities. We used B-spline as a polynomial curve fitting method to eliminate artificial line segments that are out of interest and to link discontinuous segments with similar trends. We performed statistical analyses in order to compare the final image discontinuities maps with existing references map.

Highlights

In recent years, most lineament extraction methods are either based on visual image interpretation by an expert or automatic detection by using remote sensing images [1,2,3,4]
The main goal of this study is to develop a new MATLAB based toolbox (TecLines) for automatic linear image discontinuities mapping from satellite images and digital elevation models (DEM)
The specific objective of this study is to develop a procedure that consists of the integration between Hough transform (HT) method, Tavares-Phadilha algorithm [39], and B-spline polynomial curve fitting method [11,41] to extract the curvilinear image discontinuities with consideration of the object lengths and orientations as well as the distance between neighboring line segments

Summary

Introduction

Most lineament extraction methods are either based on visual image interpretation by an expert or (semi-) automatic detection by using remote sensing images [1,2,3,4]. The automatic methods have resulted in savings of time and improve the objectivity of lineament extraction process [5,6,7,8,9,10,11,12]. Automatic lineament extraction methods are based on edge detection techniques that enhance the pixels at the edges on an image, instead of directly extracting edge contours. Most edge detection methods results contain fragmented edges and should be interpreted visually. We discussed the different methods for detecting potential edge pixels in part 1 [14]

Objectives

Results

Conclusion