{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:27:40Z","timestamp":1776216460489,"version":"3.50.1"},"reference-count":108,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.<\/jats:p>","DOI":"10.3390\/rs14020255","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0058-3579","authenticated-orcid":false,"given":"Xin","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1828-9722","authenticated-orcid":false,"given":"Sundaresh","family":"Ram","sequence":"additional","affiliation":[{"name":"Department of Radiology, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit C.","family":"Philip","sequence":"additional","affiliation":[{"name":"Genetesis, Mason, OH 45040, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey J.","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeno","family":"Szep","sequence":"additional","affiliation":[{"name":"Fraunhofer USA Center Mid-Atlantic (CMA), Riverdale, MD 20737, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1205-3890","authenticated-orcid":false,"given":"Sicong","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pratik","family":"Satam","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas","family":"Pacheco","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Universidad de Sonora, Hermosillo 83000, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salim","family":"Hariri","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"ref_1","unstructured":"Chen, G., Wang, H.-T., Chen, K., Li, Z.-Y., Song, Z.-D., Liu, Y.-L., and Knoll, A. 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