ArticleScientific reports2023
Features extraction from multi-spectral remote sensing images based on multi-threshold binarization.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- TDDC-YOLO: texture-defect disentanglement for robust wood surface crack detection under domain shifts.Scientific reports · 2026Article
- LHA-YOLO: A Lightweight and High-Accuracy Detector via Parallel Attention and Divide-and-Conquer Fusion for UAV Images.Sensors (Basel, Switzerland) · 2026Article
- SFD-YOLO for small-object fragment impact detection in warhead target-plate testing.Scientific reports · 2026Article
- Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions.Sensors (Basel, Switzerland) · 2024Article
- Deep learning assisted XRF spectra classification.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In this paper, we propose a solution to resolve the limitation of deep CNN models in real-time applications. The proposed approach uses multi-threshold binarization over the whole multi-spectral remote sensing image to extract the vector of discriminative features for classification. We compare the classification accuracy and the training time of the proposed approach with ResNet and Ensemble CNN models. The proposed approach shows a significant advantage in accuracy for small datasets, while keeping very close recall score to both deep CNN models for larger datasets. On the other hand, regardless of the dataset size, the proposed multi-threshold binarization provides approximately 5 times lower training and inference time than both ResNet and Ensemble CNN models.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.