2023 article
Assessment of Performance of Tree-Based Algorithms to Reduce Errors of Omisssion and Commission in Change Detection
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, pp. 6676–6679.
author keywords: change detection; broad area search; remote sensing; filtering; error of commission; random forest
TL;DR:
A novel pixel-based broad area search (BAS) approach that detects and classifies heavy construction, which is an important indicator of human development and of interest to the intelligence community is presented.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
15. Life on Land
(Web of Science; OpenAlex)
Source: Web Of Science
Added: March 25, 2024
The ability to detect land use and land cover change quickly and accurately is crucial for earth system modeling, policy making, and sustainable land management. Remote sensing has been widely used to map and monitor land use and land cover change over very large areas. Many change detection algorithms (CDAs) have been developed with promising accuracy. However, accuracy of detecting specific types of change using these algorithms is often not satisfactory owing to errors of commission. We present a novel pixel-based broad area search (BAS) approach that detects and classifies heavy construction, which is an important indicator of human development and of interest to the intelligence community. The BAS system combines an online CDA, roboBayes, with a supervised tree-based classifier that removes the CDA’s errors of commission. To assess the performance of the classifier, we examined three tree-based algorithms – decision tree, random forest, and LightGBM – trained on roboBayes model parameters, tuning the models using a leave-one-region-out cross-validation strategy. We compared the performance of the tree-based classifiers against a baseline of filters created by the authors. Performance was evaluated at the pixel-level using precision, recall, and F1-score, which are analogues of commission error, omission error, and accuracy, respectively. The BAS system with optimized tree-based filters performed nearly 80% better than the BAS system without any filters and more than 50% better than the authors’ filters.