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Correction published on 3 August 2018, see Remote Sens. 2018, 10(8), 1220.

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Remote Sens. 2018, 10(6), 964; https://doi.org/10.3390/rs10060964

Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Received: 11 May 2018 / Revised: 9 June 2018 / Accepted: 14 June 2018 / Published: 16 June 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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Abstract

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation. View Full-Text
Keywords: remote sensing image retrieval (RSIR); multi-label benchmark dataset; multi-label image retrieval; single-label image retrieval; handcrafted features; convolutional neural networks remote sensing image retrieval (RSIR); multi-label benchmark dataset; multi-label image retrieval; single-label image retrieval; handcrafted features; convolutional neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shao, Z.; Yang, K.; Zhou, W. Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset. Remote Sens. 2018, 10, 964.

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