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Unsupervised classification of high resolution satellite imagery by self-organizing neural network
Published October 16, 2010

The current paper discusses the importance of the modern high resolution satellite imagery. The acquired high amount of data must be processed by an efficient way, where the used Kohonen-type self-organizing map has been proven as a suitable tool. The paper gives an introduction to this interesting method. The tests have shown that the multispe...ctral image information can be taken after a resampling step as neural network inputs, and then the derived network weights are able to evaluate the whole image with acceptable thematic accuracy.

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Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery
Published July 14, 2021

In this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spect...ral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation structure of the satellite bands and the NDVI and General Linear Modeling to evaluate the AUC of different models. Although different classification methods did not result in significantly differing outcomes (AUC values between 0.96 and 0.99), SVM’s performance was better. The contribution of NDVI resulted in significantly higher AUC values. SkySat’s bands provided slightly better input data related to PlanetScope but the difference was minimal (~3%); accordingly, both satellites ensured excellent classification results.

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Correction of Atmospheric Haze of IRS-1C LISS-III Multispectral Satellite Imagery: An Empirical and Semi-Empirical Based Approach
Published June 14, 2016

The atmospheric effect greatly affects the quality of satellite data and mostly found in the polluted urban area in the great extent. In this paper, the atmospheric correction has been carried out on IRS-1C LISS-III multispectral satellite image for efficient results for the Raipur city, India. The atmospheric conditions during satellite data a...cquisition was very clear hence very clear relative scattering model of improved dark object subtraction method for the correction of atmospheric effects in the data has been carried out to produce the realistic results. The haze values (HV) for green band (band 2), red band (band 3), NIR band (band 4) and SWIR (band 5) are 79, 53, 54 and 124, respectively; were used for the corrections of haze effects using simple dark object subtraction method (SDOS). But the final predicted haze value (FPHV) for these bands are 79, 49.85, 21.31 and 0.13 that were used for the corrections of haze effects applying improved dark object subtraction method (IDOS). We found that IDOS method produces very realistic results when compared with SDOS method for urban land use mapping and change detection analysis. Consequently, ATCOR2 model provides better results when compared with SDOS and IDOS in the study.

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