Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

Gao, Xin; Ram, Sundaresh; Philip, Rohit C.; Rodríguez, Jeffrey J.; Szep, Jeno; Shao, Sicong; Satam, Pratik; Pacheco, Jesús; Hariri, Salim

doi:10.3390/rs14020255

Title: Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

Journal Article · 05 January 2022 · Remote Sensing

DOI: https://doi.org/10.3390/rs14020255 · OSTI ID:1981179

^[1];

^[2]; Philip, Rohit C. ^[3]; Rodríguez, Jeffrey J. ^[4]; Szep, Jeno ^[5];

^[4]; Satam, Pratik ^[4]; Pacheco, Jesús ^[6]; Hariri, Salim ^[4]

University of Arizona, Tucson, AZ (United States); Univ. of Arizona, Tucson, AZ (United States)
University of Michigan, Ann Arbor, MI (United States)
Genetesis, Mason, OH (United States)
University of Arizona, Tucson, AZ (United States)
Fraunhofer USA Center Mid-Atlantic (CMA), Riverdale, MD (United States)
Universidad de Sonora, Hermosillo (Mexico)

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.

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Research Organization:: University of Arizona, Tucson, AZ (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); Air Force Office of Scientific Research (AFOSR); National Science Foundation (NSF); National Institute of Standards and Technology (NIST)

Grant/Contract Number:: NA0003946

OSTI ID:: 1981179

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 2 Vol. 14; ISSN 2072-4292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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wide-area aerial imagery

Title: Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

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