結合衛星影像與地形指標於山崩自動分類之研究

Integrating Geomorphic Indexes and SPOT Multispectral Imagery for Landslides Classification

楊孟學(Ming-Lun Lu) ； 林明璋(Jing-Yi Huang) ； 劉進金(Yuh-Lurng Chung)

《航測及遙測學刊》 14卷1期 (2009/03) Pp. 11-23

https://doi.org/10.6574/JPRS.2009.14(1).2

摘要

過去遙測辨識山崩的方法，主要根據二維的影像光譜資訊及特定的分類演算法予以分類。因為利用影像光譜資訊難以將山崩與道路、屋頂及裸露地等光譜特徵值相似之地類分離，所以本研究以石門水庫上游集水區為例，選定龍王颱風襲台後之SPOT 影像以及三維空載光達(LiDAR)數值地形模型及其衍生地形指標等，進行自動分類之探討，期望能提昇山崩的分類精度。本研究使用的地形指標包括︰(1)DEM 坡度、(2)碎形維度指標、(3)地形多樣性指標(Diversity)、(4)地形優勢度指標(Dominance)、(5)地形相對複雜度指標(Relative Richness)等五項。同時以最大似然分類法(Maximum Likelihood)以及馬氏距離法(Mahalanobis)比較各種地形指標之輔助程度。結果發現，利用地形指標輔助SPOT原始四波段分類，以DEM坡度在最大似然分類法下輔助效果最佳，能減少27%以上的誤判。其次則以地形多樣性指標，約有11%的輔助效果。

關鍵字

光達 ； 地質災害 ； 影像分類

並列摘要

Conventional approach of landslide mapping is either solely on basis of spectral or spatial characteristics of the 2D images. Landslides are usually confusing with houses, roads, and other bare lands because these ground features have similar spectral patterns. In this study, 3D airborne LiDAR data are integrated with SPOT images for landslide classification for improving classification accuracy. A study area is selected in a sub-basin of Shimen Reservoir. SPOT images and LiDAR data are taken in the same month after the typhoon of Longwang in November of 2005. The LiDAR-derived data include DEM slope gradient, DSM slope gradient, and roughness data such as Fractal dimension, diversity, dominance and relative richness. These derivatives are then combined with spectral bands for classification algorithms including maximum likelihood and Mahalanobis. Comparisons are made for various approaches such as with or without LiDAR derivatives and various classification algorithms. It is concluded that with the inclusion of LiDAR derivatives and proper arrangement of classification procedures, an improvement of more than 11% of user's accuracy and 27% of producer's accuracy in diversity by maximum Likelihood classification algorithms can be achieved. Secondary, the diversity provides 11% of user's accuracy and 21% producer's accuracy by maximum likelihood classification algorithms.

並列關鍵字

Airborne LiDAR ； Geological Hazard ； Image Classification