MMIJ Annual Meeting 2017

Presentation information

企画講演

陸から海に至るビッグデータ探査技術-海洋底資源から陸上地熱資源まで-

Mon. Mar 27, 2017 9:00 AM - 12:15 PM Room-3 (6号館 3階 635講義室)

司会: 木崎彰久(秋田大学),長縄成実(東京大学),柏谷公希(京都大学)

11:30 AM - 11:45 AM

[1301-12-10] Towards spectral and spatial joint downscaling of multi-spectral satellite imagery for detailed mineral mapping

Hoang Nguyen Tien1, Katsuaki Koike1 (1. Kyoto University)

司会: 柏谷公希(京都大学)

Keywords:Remote Sensing, Spectral band, Spatial resolution, Downscaling, Mineral classification

Hyperspectral remote sensing is more effective and provides greater accuracy than multispectral remote sensing for identification of surface materials in general. Airborne Visible Infrared Imaging Spectrometer (AVIRIS), a typical airborne hyperspectral sensor, has 224 spectral bands, while the Landsat series has the longest space-based record of Earth’s land and global coverage. We have developed a hyperspectral transformation method, Pseudo-Hyperspectral Image Transformation Algorithm (PHITA) to transform Landsat 7 ETM+ imagery into pseudo-EO-1 Hyperion imagery using correlations between ETM+ and Hyperion band reflectance data. This study extends PHITA to simulate pseudo-AVIRIS image from Landsat 8 OLI image. The pseudo-AVIRIS image has the same number of high-quality AVIRIS bands with a downscaled spatial resolution of 16.4 m, half of the original resolution. The Cuprite area, known as rich in hydrothermal alteration and metals such as gold and copper, was selected for a case study. The resultant pseudo-image was verified by a general appearance, six statistical quality indices, spectral reconstructions, and mineral mapping. Most pseudo-AVIRIS bands have large Pearson’s correlation coefficient (PCC), universal image quality index (UIQI), and structural similarity index (SSIM) than 0.95 and small Root Mean Square Error (RMSE) values mostly than 0.025. These results demonstrate strong correlations between the original and pseudo-images. Spectral reflectance profiles of main endmembers show equivalent behaviors in the original and pseudo-images, except for a small difference in absorption depth. In comparison with the original AVIRIS mineral map, the pseudo-mineral map has the overall accuracy of 68% and Kappa coefficient of 0.6. These results support that PHITA is highly capable of transforming OLI image into pseudo-AVIRIS image. However, before the transformation, an additional spatial downscaling technique of OLI image is necessary to improve the mineral classification accuracy. A more advanced spatial downscaling is our next step.

講演PDFファイルダウンロードパスワード認証

講演集に収録された講演PDFファイルのダウンロードにはパスワードが必要です。

現在有効なパスワードは、[資源・素材学会会員専用パスワード]です。
※[資源・素材学会会員専用パスワード]は【会員マイページ】にてご確認ください。(毎年1月に変更いたします。)

[資源・素材学会会員専用パスワード]を入力してください

Password