Hyperspectral image secure retrieval based on encrypted deep spectral–spatial features

Zhang, Jing; Lu, Chen; Liang, Xi

doi:10.1117/1.jrs.13.018501

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Many studies have also focused on satellite High Spectral Image (HSI) processing. The analyses of HSI are very challenging and require huge computer capacities and enhanced algorithms [22]. In [23], the authors developed an intelligent algorithm to refine HSI using the super-resolution fusing method.…”

Section: Related Workmentioning

confidence: 99%

SAT-Hadoop-Processor: A Distributed Remote Sensing Big Data Processing Software for Earth Observation Applications

Semlali

Freitag²

2021

Applied Sciences

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Nowadays, several environmental applications take advantage of remote sensing techniques. A considerable volume of this remote sensing data occurs in near real-time. Such data are diverse and are provided with high velocity and variety, their pre-processing requires large computing capacities, and a fast execution time is critical. This paper proposes a new distributed software for remote sensing data pre-processing and ingestion using cloud computing technology, specifically OpenStack. The developed software discarded 86% of the unneeded daily files and removed around 20% of the erroneous and inaccurate datasets. The parallel processing optimized the total execution time by 90%. Finally, the software efficiently processed and integrated data into the Hadoop storage system, notably the HDFS, HBase, and Hive.

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Section: Related Workmentioning

confidence: 99%

SAT-Hadoop-Processor: A Distributed Remote Sensing Big Data Processing Software for Earth Observation Applications

Semlali

Freitag²

2021

Applied Sciences

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“…The final retrieval performance is significantly better than the existing methods. Moreover, Zhang et al [ 33 ] introduced correlation feedback based on feature weighting to further improve retrieval accuracy.…”

Section: Related Workmentioning

confidence: 99%

Low Dimensional Discriminative Representation of Fully Connected Layer Features Using Extended LargeVis Method for High-Resolution Remote Sensing Image Retrieval

Zhuo

Zhou

2020

Sensors

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Recently, there have been rapid advances in high-resolution remote sensing image retrieval, which plays an important role in remote sensing data management and utilization. For content-based remote sensing image retrieval, low-dimensional, representative and discriminative features are essential to ensure good retrieval accuracy and speed. Dimensionality reduction is one of the important solutions to improve the quality of features in image retrieval, in which LargeVis is an effective algorithm specifically designed for Big Data visualization. Here, an extended LargeVis (E-LargeVis) dimensionality reduction method for high-resolution remote sensing image retrieval is proposed. This can realize the dimensionality reduction of single high-dimensional data by modeling the implicit mapping relationship between LargeVis high-dimensional data and low-dimensional data with support vector regression. An effective high-resolution remote sensing image retrieval method is proposed to obtain stronger representative and discriminative deep features. First, the fully connected layer features are extracted using a channel attention-based ResNet50 as a backbone network. Then, E-LargeVis is used to reduce the dimensionality of the fully connected features to obtain a low-dimensional discriminative representation. Finally, L2 distance is computed for similarity measurement to realize the retrieval of high-resolution remote sensing images. The experimental results on four high-resolution remote sensing image datasets, including UCM, RS19, RSSCN7, and AID, show that for various convolutional neural network architectures, the proposed E-LargeVis can effectively improve retrieval performance, far exceeding other dimensionality reduction methods.

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Image retrieval from remote sensing big data: A survey

2021

Information Fusion

150

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Hyperspectral image secure retrieval based on encrypted deep spectral–spatial features

Cited by 3 publications

References 37 publications

SAT-Hadoop-Processor: A Distributed Remote Sensing Big Data Processing Software for Earth Observation Applications

SAT-Hadoop-Processor: A Distributed Remote Sensing Big Data Processing Software for Earth Observation Applications

Low Dimensional Discriminative Representation of Fully Connected Layer Features Using Extended LargeVis Method for High-Resolution Remote Sensing Image Retrieval

Image retrieval from remote sensing big data: A survey

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