光谱与纹理信息复合的土地利用/覆盖变化动态监测研究

及时、准确地动态监测地球表面特性对于掌握人类和自然现象之间的关系和相互作用是非常重要的,并为决策的制定奠定基础。传统卫星遥感的土地利用/覆盖变化动态监测方法基本上可分为光谱直接比较法和分类比较法两大类,但两类方法多以光谱信息为基础来提取土地利用变化信息,而忽略纹理等空间信息。本研究中,基于变化向量分析方法,将光谱与纹理两种信息复合计算变化强度,并采用支持向量机法提取变化/非变化信息,通过监督分类确定变化区域内的土地利用/覆盖类型的转移方向,完成土地利用/覆盖动态监测。最后,利用两期TM数据,对海淀区1997—2004年进行土地利用/覆盖变化动态监测,以验证该方法。该方法较分类后比较法在一定程度上减少误差积累,降低了错误类型转化,提取的变化信息总精度达到93.1%,Kappa为0.862,比利用光谱信息双窗口变步长的变化向量分析方法提取出土地利用/覆盖变化信息的精度有一定的提高(总体精度为90.2%,Kappa为0.804)。纹理信息与光谱信息复合,能够更大拉开变化/非变化信息之间的距离,有利于动态变化信息的提取,是该方法能够有效提取变化信息的关键所在。

Land Use/cover Change Detection with Multi-source Data

It is of great importance to obtain the earth surface property timely and effectively,which can help us to know the relationship between human and nature phenomena and also for decision-making.Pixel-based and classification based remote sensed data are the two normal methods during the traditional land use/cover change detection,which make use of the single-source spectral to extract the changed land use/cover information,while texture and other spatial information are neglected.In this research,spectral and texture information basing on the Change Vector Analysis and Support Vector Machine method are incorporated to extract the land use/cover information.The land use/cover information are extracted with the method above in Haidian district,Beijing,supported with the two-temporal TM image in 1997 and 2004,the overall accuracy and Kappa are 93.1% and 0.862 respectively,better than double-windows flexible pace searching CVA method.Whose overall accuracy and Kappa are 90.2% and 0.804 respectively,showing that the method in this paper can extract the changed information effectively.On the other hand,this method can over come the difficulty in searching the threshold which has to be engaged in the CVA method.