Abstract
The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.
Highlights
During the image capturing process of very high resolution imagery, numerous influential factors hinder the quality of these images, such as the shadows caused from the different angle of the sun, terrain features, and surface object occlusion (Dare, 2005; Tsai, 2006)
This study analyzed the characteristics of vegetation index in the shade area and non-shadow area
We compare Normalized Difference Vegetation Index (NDVI) mean values between shadow and nonshadow area, the results indicated that shadowing decline NDVI mean values from 0.64 to 0.38 in the vegetation category, increase NDVI mean values from -0.10 to 0.03 in the nonvegetation category, and increase NDVI mean values from -0.41 to -0.13 in the water body category (Table 5) (Figure 3)
Summary
During the image capturing process of very high resolution imagery, numerous influential factors hinder the quality of these images, such as the shadows caused from the different angle of the sun, terrain features, and surface object occlusion (Dare, 2005; Tsai, 2006). The shadows in remote sensing images are regarded as image nuisances in numerous applications, change detection and image classification (Dare, 2005; Zhou et al, 2009), frequently affecting the accuracy of analytical results (Wilson, 1997). The accuracy of land cover/ use mapping procedure over steep mountainous terrain is often low (Dorren et al, 2003; Shahtahmassebi et al, 2013). In Taiwan, the landscape is often characterized as alpine terrain, using very high resolution images for land cover/ use mapping will be severely affected by the shadow problem. The classification and interpretation of the shaded area in remote sensing image is a challenge, because of the reduction or total loss of spectral information in those areas (Dare, 2005; Yuan, 2008; Zhou et al, 2009), the importance of understanding the spectral characteristics of such areas, for fundamental analysis
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