Research of Air Pollution by Dust Aerosols During Construction

Jaremenko, S A; Garmonov, K. V.; Sheps, Roman

doi:10.1088/1757-899x/262/1/012189

Cited by 1 publication

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“…The presence of specific disposed or waste material causes the aerosol bands to help detect dump sites. It is also our assumption that the port areas and construction sites from which the dataset was obtained consisted of specific amounts of fly-ashes and aerosols [72], which lead to a better assessment of these classes in this group. • Surprisingly, continuous and discontinuous urban fabrics were also captured well by this band group.…”

Section: Understanding Band Propertiesmentioning

confidence: 99%

Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive

Chaudhuri

Dey

Datcu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Conventional remote sensing data analysis techniques have a significant bottleneck of operating on a selectively chosen small-scale dataset. Availability of an enormous volume of data demands handling large-scale, diverse data, which have been made possible with neural network-based architectures. This work exploits the contextual information capturing ability of deep neural networks (DNNs), particularly investigating multispectral band properties from Sentinel-2 image patches. Besides, an increase in the spatial resolution often leads to non-linear mixing of land-cover types within a target resolution cell. We recognize this fact and group the bands according to their spatial resolutions, and propose a classification and retrieval framework. We design a representation learning framework for classifying the multi-spectral data by first utilizing all the bands and then using the grouped bands according to their spatial resolutions. We also propose a novel triplet-loss function for multi-labeled images and use it to design an inter-band group retrieval framework. We demonstrate its effectiveness over the conventional tripletloss function. Finally, we present a comprehensive discussion of the obtained results. We thoroughly analyze the performance of the band groups on various land-cover and land-use areas from agro-forestry regions, water bodies, and human-made structures. Experimental results for the classification and retrieval framework on the benchmarked BigEarthNet dataset exhibit marked improvements over existing studies.

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Section: Understanding Band Propertiesmentioning

confidence: 99%