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Parallel discriminative subspace for city target detection from high dimension images

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Abstract

City Target Detection is an enduring problem that intrigues the researchers all over the world. The great success of existing Target Detection algorithm appears in ubiquitous scenarios: Pedestrian Detection, Vehicle Tracking, etc. However, as for the city target detection in the remote sensing, we are facing with two inevitable problems: Complex Environment and Massive Information. The complicated environment encumbers the accurate extraction of the target profile, and the huge amount of information turns it into a heavy workload to get the final outcome for the conventional CPU- compiler architecture. In this paper, we propose a binary hypothesis framework based on adaptive dictionary and discriminative subspace for hyperspectral city target detection (BHADDS). Furthermore, we have also implemented it on other hardware platform alongside with CPU, such as FPGA. FPGA is a low-power portable and programmble SoC, and also the protocol model for potential massive production of the SoC chipset. Our eventual aim is heading for the high-performance processor with strong instant processing ability for remote sensing. In the final part of the paper, we have given a comprehensive performance comparison over the different platforms and summarized their applicable scenarios.

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Acknowledgement

This work was supported in part by the National Key R & D Program of China under Grant 2018YFA060550 and the National Natural Science Foundation of China under Grants 41871243, the Natural Science Foundation of Hubei Province under Grants 2018CFA050, and the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) under Grant 2019AEA170.

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Author notes

  1. Yipeng Zhang and Yiming Zhang contributed equally to this work.

Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software , School of Computer Science and Institute of Artificial Intelligence, Wuhan University, Wuhan, China

    Yipeng Zhang, Bo Du, Chao Zhang, Xiaoyang Guo & Weiping Tu

  2. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China

    Yiming Zhang

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  1. Yipeng Zhang
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  2. Yiming Zhang
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Correspondence to Bo Du or Weiping Tu.

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Zhang, Y., Zhang, Y., Du, B. et al. Parallel discriminative subspace for city target detection from high dimension images. Geoinformatica 26, 299–322 (2022). https://doi.org/10.1007/s10707-020-00399-7

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  • DOI: https://doi.org/10.1007/s10707-020-00399-7

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