• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.283-295
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• 2023

With the development of sensor and satellite technology, numerous high-resolution and multi-spectral satellite images have been available. Due to their wavelength-dependent reflection, transmission, and scattering characteristics, multi-spectral satellite images can provide complementary information for earth observation. In particular, the short-wave infrared (SWIR) band can penetrate certain types of atmospheric aerosols from the benefit of the reduced Rayleigh scattering effect, which allows for a clearer view and more detailed information to be captured from hazed surfaces compared to the visible band. In this study, we proposed a multi-resolution transform-based image fusion method to combine visible and SWIR satellite images. The purpose of the fusion method is to generate a single integrated image that incorporates complementary information such as detailed background information from the visible band and land cover information in the haze region from the SWIR band. For this purpose, this study applied the Laplacian pyramid-based multi-resolution transform method, which is a representative image decomposition approach for image fusion. Additionally, we modified the multiresolution fusion method by combining a haze-guided weight map based on the prior knowledge that SWIR bands contain more information in pixels from the haze region. The proposed method was validated using very high-resolution satellite images from Worldview-3, containing multi-spectral visible and SWIR bands. The experimental data including hazed areas with limited visibility caused by smoke from wildfires was utilized to validate the penetration properties of the proposed fusion method. Both quantitative and visual evaluations were conducted using image quality assessment indices. The results showed that the bright features from the SWIR bands in the hazed areas were successfully fused into the integrated feature maps without any loss of detailed information from the visible bands.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.695-706
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• 2022

Medium and high-resolution optical satellites have proven their effectiveness in detecting wildfire areas. However, smoke plumes generated by wildfire scatter visible light incidents on the surface, thereby interrupting accurate monitoring of the area where wildfire occurs. Therefore, a technology to extract smoke in advance is required. Deep learning technology is expected to improve the accuracy of smoke extraction, but the lack of training datasets limits the application. However, for clouds, which have a similar property of scattering visible light, a large amount of training datasets has been accumulated. The purpose of this study is to develop a smoke extraction technique using deep learning, and the limits due to the lack of datasets were overcome by using a cloud dataset on transfer learning. To check the effectiveness of transfer learning, a small-scale smoke extraction training set was made, and the smoke extraction performance was compared before and after applying transfer learning using a public cloud dataset. As a result, not only the performance in the visible light wavelength band was enhanced but also in the near infrared (NIR) and short-wave infrared (SWIR). Through the results of this study, it is expected that the lack of datasets, which is a critical limit for using deep learning on smoke extraction, can be solved, and therefore, through the advancement of smoke extraction technology, it will be possible to present an advantage in monitoring wildfires.

• Economic and Environmental Geology
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• v.52 no.4
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• pp.259-274
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• 2019

Using portable XRF and SWIR analyzer, the characteristics of element dispersion and wallrock alterations induced by interaction between hydrothermal fluids and host rocks were investigated and ore exploration factors were estimated for the orogenic-type Samgwang Au deposits. On this purpose, in-situ measurements were conducted for 804 spots at regular intervals with a total of 4,824 times for host rocks, consisting of schist and gneiss, and altered wallrocks contacted with quartz veins in the Bonhang adit of the deposit, and the results were compared with quantitative data obtained by XRF and ICP analysis. The regression coefficients are 0.88 for major elements and 0.56 for trace elements, excluding V. For polished rock slabs, better results came out for major elements, 0.97 and for trace elements, 0.65. In altered wallrocks contacted with quartz veins, elements such as Fe, Zn, and Rb exhibit positive correlations with As in concentrations, while V forms a negative trend. Contour maps demonstrate that As, Zn, Rb, Fe, Ti, Cr, and Ni are enriched together near quartz veins, showing similar elemental behaviors. In-situ analysis using portable SWIR analyzer represents that schist and gneiss contain mica, illite, chlorite, sericite, amphibole, and epidote, while illite, sericite, gypsum, and mica are present in the altered rocks contacted with quartz veins. In contour maps, chlorite occurs mostly in host rocks, while sericite is concentrated near quartz veins. These results are similar to those of previous studies for element dispersion and hydrothermal alteration, and support the possibility for application of in-situ analysis on the exploration of orogenic gold deposit.