Landuse/Landcover Change Detection Using Two-stage Remote Sensing Image Classification
Date Issued
2006
Date
2006
Author(s)
Chung, Chien-Wen
DOI
zh-TW
Abstract
Remote sensing images and technologies have been widely applied to environmental monitoring, in particular landuse/landcover (LULC) classification and change detection. The accuracy of LULC classification depends on the spatial resolution of remote sensing images, features (spectral or textural) adopted for classification, the desired landcover classes, and also the classification method. In cases where complex landuses are present and detailed LULC classes are desired, it is often difficult to achieve high level of classification accuracy. In this study, a two-stage Bayesian classification approach was proposed to circumvent such difficulties. In the first stage, only spectral features were adopted for coarse classification (bare land, agriculture, water body, grassland, and forest). Then, textural features were considered to conduct within-class classification in the second stage. The bare land class was divided into bare soil and built-up and the agriculture class was divided into orchard, vegetation garden, and tea plantation. Application of the proposed approach in the Chi-Jia-Wuan Creek watershed in central Taiwan for 2004 and 2005 yields about 98% overall accuracy in the first stage and 86% and 89% overall accuracies in the second stage.
For LULC change detection, a hypothesis-test-based multispectral algorithm was developed. The whole study area was classified into three major classes - forest, water body and bare land using multi-date (2004 and 2005) and multispectral images. Such coarse classification can achieve high level of classification accuracies. No-change pixels of individual classes were then identified and used as the basis for establishing 95% confidence intervals for LULC change detection. The first principal component of the original multispectral features of 2004 (PCX1) and the first principal component of the multe-date multispectral differences (PCΔX) were used to construct bivariate normal distributions for the three major LULC classes. Then, given the value of PCX1, the conditional probability distribution of PCΔX can be spefied. Therefore, under the null hypothesis of no change, the 95% confidence intervals of
individual LULC classes can be established. Using a set of validation data, the proposed change detection algorithm is shown to be capable of achieving high accuracies.
Subjects
階段式貝氏分類法
紋理特徵
發散度
變遷偵測
第一主成分
條件機率
Two-Stage Bayesian Classification
Textural Feature
Change Detection
Principal Component
Conditional Probability
SDGs
Type
thesis
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