Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70011366

Objective procedures for lineament enhancement and extraction

G. K. Moore·F. A. Waltz
View original source ↗Export metadata ↓

Abstract

A longterm research goal at EROS Data Center is to develop automated, objective procedures for lineament mapping. In support of this goal, a five-step digital convolution procedure has been used to produce directionally enhanced images, which contain few artifacts and little noise. The main limitation of this procedure is that little enhancement of lineaments occurs in dissected terrain, in shadowed areas, and in flat areas with a uniform land cover. The directional enhancement procedure can be modified to extract edge and line segments from an image. Any of various decision rules can then be used to connect the line segments and to produce a final lineament map. The result is an interpretive map, but one that is based on an objective extraction of lineament components by digital processing. -from Authors

Explore related subjects

Photogrammetric Engineering and Remote SensingArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

G. K. Moore, F. A. Waltz. 1983. Objective procedures for lineament enhancement and extraction. https://pubs.usgs.gov/publication/70011366

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2025

Evaluating Three-Dimensional Elevation Program lidar consistency and accuracy at scale using cloud-native, open-source methods

The U.S. Geological Survey three-dimensional elevation program (3DEP) has significantly expanded national lidar coverage, necessitating scalable, reproducible methods for assessing data quality across diverse terrains and acquisition conditions. This study introduces a cloud-native, open-source workflow designed to evaluate the geometric accuracy and consistency of 3DEP lidar data sets at a national scale. Leveraging tools such as the Point Data Abstraction Library, Open3D, and Amazon Web Services infrastructure, the workflow integrates global navigation satellite system‐surveyed ground control points and terrestrial laser scanning data to validate airborne lidar collections. Two case studies demonstrate the application of this process. In Puerto Rico, the process identified vertical biases and inconsistencies in vegetated areas, while in Iowa and Arizona, the process confirmed high vertical accuracy with minimal bias. The results underscore the effectiveness of combining cloud computing with open-source tools to perform large-scale lidar data quality assessments. This process offers a reproducible, efficient solution for nationwide validation of 3DEP data sets, supporting enhanced decision-making in geospatial applications.

Photogrammetric Engineering and Remote Sensing↗
USGS2025

Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data

Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.

California↗