城镇化背景下基于时序TM/NDVI的建成区检测方法——以福州市为例

对1990—2010年49景福州市TM时间序列影像进行处理,采用MODTRAN4+模型进行大气校正,得到研究区土地覆盖类型的NDVI值的多时相轨迹图。分析城镇化背景下建成区的变化特征和NDVI时间序列数据的季节特征,添加耕地发展为建设用地的地物特征到学习样本,比较不同数据组合对最大似然法、支持向量机、神经网络法、面向对象法对分类和检测城镇化背景下建设用地精度的影响,以及比较添加样本特征后对城镇化进程中建设用地检测方法的影响。结果表明,对于小样本数据集,面向对象法具有最高的分类精度,不同的数据组合与不同季节对面向对象法分类精度的影响分别达3.49%和5.22%,引入NDVI时间序列数据和添加变化地物的学习样本,总体分类精度提高了3.54%,建设用地的制图精度提高了4.24%。

Monitoring land cover change in urban and peri-urban area using dense time of TM/NDVI data: a case study of Fuzhou city

Atmospheric corrections were conducted with the MODTRAN4+ model for 49 TM data from 1990 to 2010 in Fuzhou. Multi-temporal trajectories of major land cover type were derived from NDVI images. The trends of Mean NDVI were analyzed. To investigate the influence of different data combination on the classification and detection accuraly of different methods,induding maximum likelihood classification, support vector machine, artificial neural network, and object-oriented methods, and compared the deteetion methods before and after adding a sample, the areas converted from cropland to built-up land were added to the learning sample. The results show that the object-oriented method is the most accurate method compared with other methods for a small sample. By using the method, the classification accuracy improves up to 3.53% and 4.24% for different data combination and different season respectively.Adding NDVI data and the sample of changing features improves 3.54% of the whole classification accuracy and 4.24% of drawing accuracy of the buid-up land.