Integrating airborne SAR, Landsat TM and airborne geophysics data for improving geological mapping in the Amazon region: the Cigano Granite, Carajás Province, Brazil

Teruiya, R. K.; Paradellá, Waldir Renato; Santos, Athos Ribeiro dos; Dall’Agnol, Roberto; Veneziani, Paulo

doi:10.1080/01431160801891838

Cited by 24 publications

(11 citation statements)

References 28 publications

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“…Additionally, fine spatial resolution is beneficial, as it reduces the number of elements represented within a pixel, which enhances the unmixing results and thereby the detection of minerals. The spatial and spectral resolutions of Landsat TM and MODIS have been found to be too coarse for determining mineral composition (Dobos et al, 2000;Kettles et al, 2000;Teruiya et al, 2008). However, the combination of Landsat TM data and ASTER data has been useful because the general lithological variability is mapped with Landsat TM whereas ASTER maps the different mineral groups.…”

Section: Mineralogymentioning

confidence: 96%

The use of remote sensing in soil and terrain mapping — A review

et al. 2011

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Section: Mineralogymentioning

confidence: 96%

The use of remote sensing in soil and terrain mapping — A review

et al. 2011

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“…Figures 3 and 4 clearly show that the upscaled image with a coarser spatial resolution achieved the lowest accuracy in all feature sets. This is because the nearest neighbor resampling not only loses the spatial details but also increases the proportion of mixed pixels by artificially combining four adjacent pixels into one, both of which have a detrimental effect on classification accuracy [67]. Figures 3 and 4 show clearly that based solely on spectral bands, the OA increased by 1.84% to 3.85% by different pansharpening algorithms compared to that of upscaling.…”

Section: Differences Between Upscaling and Downscalingmentioning

confidence: 98%

Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

Zheng

Chen

et al. 2017

Remote Sensing

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Land Use and Land Cover (LULC) classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS), nearest neighbor diffusion (NNDiffusion), PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF) and high-pass filter fusion (HPF). Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM) and extended attribute profiles (EAPs), are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF) is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA)-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet the requirements of regional LULC applications, and the GLCM and EAPs spatial features can be used to obtain more precise classification maps.

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“…The technique developed by Cloude and Pottier [27] considers that the information contained in the coherence matrix [T] is the result of the contribution of the three types of scattering mechanisms, in which each scattering is modeled by a canonical target represented by its scattering matrix , based on the expression: (5) where are the eigenvalues of and its related eigenvector. The relative importance of each scattering to the value of is given by the eigenvalue derived from the coherence matrix [27].…”

Section: Methodsmentioning

confidence: 99%

“…The use of radar images in geological surveys is a well-established procedure, and has been employed in several studies in the moist tropics, such as integrated, multisource data procedures [1][2][3][4][5], monoscopic and stereoscopic visual analysis [6], and digital classification based on textural attributes [7]. In all these cases, the data were analyzed based on the amplitude or intensity of the backscattered signal.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region

et al. 2013

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This study evaluates the potential of C-and L-band polarimetric SAR data for the discrimination of iron-mineralized laterites in the Brazilian Amazon region. The study area is the N1 plateau located on the northern border of the Carajá s Mineral Province, the most important Brazilian mineral province which has numerous mineral deposits, particularly the world's largest iron deposits. The plateau is covered by low-density savanna-type vegetation (campus rupestres) which contrasts visibly with the dense equatorial forest. The laterites are subdivided into three units: chemical crust, iron-ore duricrust, and hematite, of which only the latter two are of economic interest. Full polarimetric data from the airborne R99B sensor of the SIVAM/CENSIPAM (L-band) system and the RADARSAT-2 satellite (C-band) were evaluated. The study focused on an assessment of distinct schemes for digital classification based on decomposition theory and hybrid approach, which incorporates statistical analysis as input data derived from the target decomposition modeling. The results indicated that the polarimetric classifications presented a poor performance, with global Kappa values below 0.20. The accuracy for the identification of units of economic interest varied from 55% to 89%, albeit with high commission error values. In addition, the results using L-band were considered superior OPEN ACCESS Remote Sens. 2013, 5 3102 compared to C-band, which suggest that the roughness scale for laterite discrimination in the area is nearer to L than to C-band.

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Integrating airborne SAR, Landsat TM and airborne geophysics data for improving geological mapping in the Amazon region: the Cigano Granite, Carajás Province, Brazil

Cited by 24 publications

References 28 publications

The use of remote sensing in soil and terrain mapping — A review

The use of remote sensing in soil and terrain mapping — A review

Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region

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