The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning
Abstract
The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs). In this paper, a novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning. First, deep Boltzmann machine is adopted to infer the spatial and structural information encoded in the low-level and middle-level features to effectively describe objects in optical RSIs. Then, a novel WSL approach is presented to object detection where the training sets require only binary labels indicating whether an image contains the target object or not. Based on the learnt high-level features, it jointly integrates saliency, intraclass compactness, and interclass separability in a Bayesian framework to initialize a set of training examples from weakly labeled images and start iterative learning of the object detector. A novel evaluation criterion is also developed to detect model drift and cease the iterative learning. Comprehensive experiments on three optical RSI data sets have demonstrated the efficacy of the proposed approach in benchmarking with several state-of-the-art supervised-learning-based object detection approaches.
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Cites
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Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400
Remote Sensing Image Scene Classification: Benchmark and State of the Art
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Self-citationAID: A benchmark data set for performance evaluation of aerial scene classification
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Object detection in optical remote sensing images: A survey and a new benchmark
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Self-citationWeakly Supervised Object Localization and Detection: A Survey
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Self-citationOn Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
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Learning a Rotation Invariant Detector with Rotatable Bounding Box
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Metadata
- Authors
- Han, J., Zhang, D., Cheng, G., Guo, L., & Ren, J.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2015-06
- Volume
- 53
- Issue
- 6
- Pages
- 3325-3337
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2085625911
- Cited by
- 733
- References
- 4
- FWCI
- 74.11
- Type
- journal article
- Language
- en
- Open access
- closed
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