Search

In recent years, automatic lineament extraction has gained huge popularity as it circumvents the issues such as errors and time constraints associated with manual extraction procedure. Another impediment in the lineament extraction process is that linear features existing near sub-surface or occluded due to vegetation cover cannot be identified solely by optical data. To mitigate these issues, a multi-sensor fusion-based automatic extraction process using EO-based optical and SAR data is proposed. Use of moderate resolution optical data such as ASTER minimizes erroneous identification and misclassification of anthropogenic features as geological features. In addition to this, a component loading-based band selection approach is proposed to select the most informative bands from the available optical bands for improved extraction of lineament features. Finally, a new HSV-based mapping method for visualization and directional analysis of the extracted linear features is discussed. The results are validated using the published seismo-tectonic map of the region and field investigation studies which corroborates that the proposed algorithm can successfully extract most of the geological lineaments in the study area with minimum errors. Research highlights: This research sheds light on the use of multi-sensor data for understanding the tectonic setup of an area. This research is highly useful for the practices such as geological mapping and weak zone demarcation using EO data of the denied areas Further, the outcome of the results can also be used in mass movement vulnerability assessment of the area for mitigation and planning purposes. Further, this also increases robustness in band selection procedure for lineament extraction. [ABSTRACT FROM AUTHOR]