基于多点信息统计法的土地覆盖图像超分辨率重建方法

A Super-Resolution Reconstruction Method for Land Cover Maps Using Multiple-Point Statistics

在利用遥感技术对土地覆盖情况制图的过程中,超分辨率重建被广泛采用.包含土地覆盖图像特征的先验模型可以减少重建过程中的不确定性.多点信息统计法可以从先验模型提取其本质特征,然后将特征复制到待模拟区域.然而传统基于线性降维的多点信息统计法不能有效处理非线性数据,因此将等距特征映射引入到多点信息统计法以实现非线性数据的降维,再对降维后的数据作分类处理.比较当前数据事件和各个分类的均值,从与当前数据事件最接近的类中提取模式.同时利用低精度原始图像作为软数据以重建较高精度的土地覆盖图像.实验表明重建的超分辨率土地覆盖图像具有与参照图像相似的结构特征.

When using remote sensing for land cover mapping, super-resolution reconstruction is widely used. Prior models containing the features of land cover maps can constrain the uncertainty of reconstruction. These prior models can be used properly by multiple-point statistics （MPS） by extracting the intrinsic features from them, and copying these features to the simulated regions., However, because traditional MPS methods based on linear dimensionality reduction are not suitable to deal with nonlinear data, isomeWic mapping （ISOMAP） is introduced in MPS to reduce the dimensionality reduction of non- linear data and then these lower-dimensional data are c/assified. Current data event.and the average of every classified class are compared so that a pattern can be extracted, from the class that is closest to the current data event. Besides, the low-reso- lution original image is viewed as soft data for generating super-resolution land cover maps. Tests show that the super-resolu- tion＇reconstructions of land cover maps have the similar structural features with those of reference images.