リモートセンシング画像の分解能が土地被覆分類精度に及ぼす影響

Abstract

Now various kinds of high resolution remote sensors are available, such as Landsat TM (spatial resolution 30 m × 30 m), SPOT HRV (20 m × 20 m) and ADEOS AVNIR (16 m × 16 m) launched in 1996. It is significant to quantify the accuracy of interpretation or classification on high resolution remotely sensed images. This paper assesses land cover classification accuracy quantitatively for four remotely sensed images acquired almost simultaneously with various sensors with different spatial resolutions: Landsat TM, SPOT HRV, JERS OPS (18 m × 24 m), Airborne multispectral scanner (AMSS: 6.25 m × 6.25 m), and false remotely sensed images produced by three degradation algorithms. These false images have 10-m to 30-m resolution resampled from the 6.25-m AMSS image. The authors also produce the 16-m false AVNIR image by a degradation algorithm, and predict the classification accuracy of ADEOS AVNIR images.