Automatic Lineaments Extraction using the Line Algorithm in the Denguélé District (North West of Ivory Coast)

The aim of this work was to apply the LINE Algorithm (Segment Extraction Algorithm) on Landsat 8 images for automatic lineament extraction in the Denguélé district. The Landsat 8 images had previously been subjected to the technique of Principal Component Analysis (PCA). After that, we implemented the LINE algorithm. Indeed, the LINE algorithm uses the following six (6) parameters : RADI (Radius of the filter) for improving the quality of the input image, GTHR (Threshold of the contour gradient), LTHR (Threshold of the contour length), FTHR (Threshold of mounting error), ATHR (Angular difference threshold between two contours ) and DTHR (Distance chaining threshold to link two contours ) for lineament discrimination. Analysis of the principal components PCA 1, PCA2 and PCA3 of bands 1, 2, 3, 4, 5 and 7 of the Landsat 8 images shows that they contain respectively 79.57; 15.88 and 2.15%, this represents overall 97.6% of all channels. 3468 lineaments were extracted. The minimum and maximum lengths of the lineaments extracted are respectively 4201.08 m and 16167.59 m and their cumulative length is 18 919 517.9 m. The lineaments average lengths are 5.55 km; 5.75 km; 5.6 km and 5.40 km respectively for NE-SW, NS, E-W and NW-SE directions. The analysis of the directions of the lineaments using a rose diagram with 10 ° of frequency, shows that the dominant directions are NE-SW (31.83% of the total lineaments), EW (28.71% of the total lineaments) and NS (27.91% of the total lineaments).

Lithological mapping and automatic lineament extraction using Aster and Gdem data in the Imini-Ounilla-Asfalou district, South High Atlas of Marrakech, Morocco

Lithological and lineament mapping using remote sensing is a fundamental step in various geological studies, as it forms the basis for the interpretation and validation of the results obtained. There were two objectives for this study, applied in the Imini-Ounilla-Asfalou district, South High Atlas of Marrakech region: first, lithological mapping by satellite image processing techniques such as ASTER L1B (hight spectral and spatial resolution), namely Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), as well as the application of three types of supervised classification, namely Spectral Angle Mapper (SAM), Maximum Likelihood (ML) and Minimum Distance (MD), on the visible/near-infrared (VNIR) and short-wave infrared (SWIR) spectral bands of our ASTER image; second, an analysis of the distribution of lineaments by automatic extraction using a Global Digital Elevation Model (GDEM) and the PC1 image derived from the PCA transformation applied to the satellite image. The best results are highlighted by the delineation of new facies in relation to the existing map; after confirmation in the field, all of these facies, which include Eocene, Triassic and Jurassic formations, are represented on the new map. The results of lineaments showed that each of them systematically shows a similarity in terms of concentration and orientation, with four preferential oriented systems: NE-SW, E-W, NNE-SSW and NW-SE. The lineaments mainly follow those of the major fault zones, with high concentrations in the northeast and southwest parts of the study area.

Comparison of Multi-Resolution Optical Landsat-8, Sentinel-2 and Radar Sentinel-1 Data for Automatic Lineament Extraction: A Case Study of Alichur Area, SE Pamir

Lineament mapping, which is an important part of any structural geological investigation, is made more efficient and easier by the availability of optical as well as radar remote sensing data, such as Landsat and Sentinel with medium and high spatial resolutions. However, the results from these multi-resolution data vary due to their difference in spatial resolution and sensitivity to soil occupation. The accuracy and quality of extracted lineaments depend strongly on the spatial resolution of the imagery. Therefore, the aim of this study was to compare the optical Landsat-8, Sentinel-2A, and radar Sentinel-1A satellite data for automatic lineament extraction. The framework of automatic approach includes defining the optimal parameters for automatic lineament extraction with a combination of edge detection and line-linking algorithms and determining suitable bands from optical data suited for lineament mapping in the study area. For the result validation, the extracted lineaments are compared against the manually obtained lineaments through the application of directional filtering and edge enhancement as well as to the lineaments digitized from the existing geological maps of the study area. In addition, a digital elevation model (DEM) has been utilized for an accuracy assessment followed by the field verification. The obtained results show that the best correlation between automatically extracted lineaments, manual interpretation, and the preexisting lineament map is achieved from the radar Sentinel-1A images. The tests indicate that the radar data used in this study, with 5872 and 5865 lineaments extracted from VH and VV polarizations respectively, is more efficient for structural lineament mapping than the Landsat-8 and Sentinel-2A optical imagery, from which 2338 and 4745 lineaments were extracted respectively.

Automatic lineament extraction from potential-field images using the Radon transform and gradient calculation

Linear anomalies are critical in the interpretation of gravity and magnetic data. Visual identification of lineaments is usually done by experienced interpreters, and identification results then have to undergo a digitization or import procedure. The traditional identification method has unavoidable subjectivity and inefficiency. To overcome these limitations, we fuse the Radon transform (RT) with gradient calculation to process gravity or magnetic data and to realize automatic detection and extraction of lineaments. As part of the detection procedure, we define the RT-based mean gradient (MG), effective mean gradient (EMG), and residual mean gradient (RMG) in order to highlight long linear segments or to enhance short linear ones in the transform domain. The gradient forms are applied self-adaptively and self-selectively to gravity or magnetic images according to specific conditions. Gradient directions are also taken into account in the transformation procedure to emphasize the characteristics of linear anomalies. To extract the position and length of the detected lineaments from the transform domain, a constraint inverse searching method (CISM) is given and used to locate the starting and end points of the lineaments. The method can deal with the condition that there is at least one linear section in a specific direction or that separate linear sections may belong to one lineament. Through tests with synthetic images and with real data from the Haijiao upheaval area in the East China Sea Basin, the detection and extraction methods are shown to be more effective and robust than the conventional RT applications. The results from the real data roughly coincide with major geologic faults that are visually identified. These results show that the methods constitute a useful tool to aid fault interpretation.