地物大小、对象尺度、影像分辨率的关系分析
摘要
Information extraction from remote sensing data encountered a new challenge while the spatial resolution is increasing quickly.People suppose that the higher the spatial resolution is,the better the result of objects classification is.To prove this guess we use two approaches: pixel-based classification and object-oriented analysis.The former site test shows one class has different precision from various resolution images.Some classes improve their precision with the high resolution but others don't.Without various resolution image data,how can we acquire the best information with only one image? Object-oriented approach offers a good solution with its key technology: multi-scale image segmentation. Object-oriented image analysis does not classify single pixel but rather image objects.Not only spectral information but also spatial,physical and contextual characteristics of image objects are used for classification.The site result shows that objects have their best scale image levels to class.The precision of object-oriented approach is much higher than those of based-pixel approach.It makes us believe that this process is the best selection for high-resolution image analysis.Two test results have proved that all objects couldn't be extracted well from the same resolution or scale.They need the corresponding resolution images or scale image levels.Multi-scale image analysis is the perfect method.