陆地卫星数据信息提取的一个新方法

陆地卫星图像的磁带数码值或扫描密度值,均可被转换成绝对辐射值。四个波段中的每一波段的辐射值,均可分解为具有不同物理意义的三部分,其中,四个波段中物理意义相同的那一部分,又可被重新组合起来,于是,便得到三个新变量。它们分别被命名为总辐射水平值L,可见-红外(辐射)平衡值B,和波段(辐射)变化向量值V。L能监测地物的辐射强度;B能监测水、水分和地表层的潮湿程度,并能区分云、雪和冰;V能监测植被的密度与生活力,因而是植被状况的良好指标。这三个变量几乎包含了原四个波段的全部信息,因此,通常只需处理这三个变量。算法基于回归分析且维数低,因此十分简单。因而,可节省图像处理时间和费用。使用这个方法,可以获得真正三色的、接近于自然色彩的真彩色合成图像,还可获得相应于地物特征的有意义的密度分割图像。此方法已被应用于中国和世界范围内的遥感资料。

A New Method for Extracting Information from Landsat Data

The digital counts or the scanned densities of the Landsat MSS image were, transformed into absolute radiance values. Each radiance of the four bands were decomposed into three parts with different significances and then the parts with the similar significance from the four bands were recombined. As a result, we obtained three new variables. Which are named respectively the general radiance level (L), the visible-infrared (radiance) balance (B) and the band radiance) variation vector (V). The L is powerful in monitoring radiation intensities of ground features; the B is powerful in mainly monitoring the water, the moisture or wetness of ground features and in differentiating the cloud, snow and ice among them; the V is powerful in monitoring density of plant cover and its vigor and hence is an excellent indicator of vegetation conditions. Those three variables contain nearly all the information of the original four bands, so we need only manipulating the three instead of four variables. The algorithm is based on the regression analysis and is very simple. Lower dimension and simpler calculation will lead to time and monev saving.Using this method, we can obtain "real" (not false) color composites, which are real three-coloured and near to the natural color. And we can obtain significant, corresponding to the ground feature characteristics, density slicing images.The methodology described above was applied to the data on hand from China and Canada and other data found in literatures from Asia, Europe, Africa, Australia, Oceania and North and South America.