基于知识库的像斑光谱向量相似度土地覆盖变化检测方法

Abstract

土地利用/覆盖变化检测是国内外全球化进程研究的重要内容,选择适当的变化检测方法对西北地区土地利用/覆盖变化进行研究在"生态十年项目"中具有重要的意义.选择西北地区具有典型代表性的TM轨道号134033区域作为变化检测方法验证的试验区,采用2005和2010年两期Landsat TM影像,在eCognition Developer 8.64软件支持下,采用基于像斑的光谱特征特征向量相似度方法进行变化检测,并利用2010年土地覆盖数据作为先验知识库对变化区域分类,提取土地利用/覆盖变化信息,并对变化结果进行定量分析.结果表明,采用基于像斑的光谱特征特征向量相似度方法对于试验区的土地利用/覆盖变化制图具有检测快速、检测精度高等优点,适合试验区以及整个西北地区的土地利用/覆盖变化的检测.最终采用该方法以及分类后比较法获得了西北地区2000-2010年近10年的土地利用/覆盖分类图.;Land use/cover change (LUCC) is an important field of global and local environmental change research. LUCC and its corresponding effects have a direct impact on the environment and ecological processes, and in turn natural resources management and related decisions. In China, LUCC research is at the core of all issues related to sustainable development, with tremendous practical significance. Remote sensing imagery change detection has great application value in many areas, such as the survey of land-use change, urban expansion, and vegetation resource monitoring. The choice of an effective change detection method for a given study area is a central issue of such detection. In research conducted in various countries, remote sensing imagery change detection methods have transitioned from pixel level to feature level and knowledge level. Currently used LUCC detection methods are in two categories, direct spectrum comparison and comparison of classification results. The spectrum feature vector of a segment in a Landsat Thematic Mapper (TM) image can be regarded as a vector of six-dimensional feature space. If the angle between two vectors is smaller and vector mode closer, the more similar are the two vectors. Thus, we used the cosine of the angle between two vectors and the ratio of vector mode to establish a vector similarity index for measuring vector similarity. Combined with the National Change of Ecological Environment Decade (2000-2010) Remote Sensing Survey and Assessment project, jointly organized and implemented by the Ministry of Environmental Protection and Chinese Academy of Sciences, this paper focuses on application of a change detection method. This method uses segment similarity of a spectrum vector based on a knowledge base in the northwest region. There is also an evaluation of method accuracy. The area covered by TM path 134 row 33, representative of the northwest region, was chosen as a change detection method validation test site. We used 2005 and 2010 two-phase Landsat TM imagery to detect land-cover change, using spectrum vector similarity based on a segment with support from eCognition Developer 8.64 software. We used 2010 land-cover data as a priori knowledge base to classify regions of change. The results showed the following. 1) Segment similarity of the spectrum vector method is appropriate for change detection in the northwest region, and accuracy of the 2005 land-cover database established using the 2010 land-cover database as a priori knowledge was relatively high. 2) The method of segment similarity of the spectrum vector has many advantages, such as less computation, fast operation, and suitability for large-scale rapid change detection. 3) The method is more effective for farmland, water, built-up land and vegetation cover change. 4) Accuracy of the land-cover database established by change detection depends on accuracy of the baseline land-cover database. Compared with the latter database, accuracy of the former database declined slightly. The main cause of this decline is change that was difficult to detect and change area misclassification, attributable to land-cover types with similar spectral features such as lakes and reservoirs, bare land, and sparse vegetation. 5) Because of the effects of imagery phase and cloud coverage, the land-cover database established by change detection requires additional manual modifications to improve its accuracy.