基于视觉复杂度的自适应尺度遥感影像分割

An Adaptive Scale Segmentation for Remote Sensing Image Based-on Visual Complexity

遥感影像中的对象尺度差异巨大,任何单一尺度的分割很难产生令人满意的结果。该文认为可以根据场景的视觉复杂程度选择合适的分割尺度,并据此提出一种自适应尺度的分割算法。根据Watson视觉模型计算场景复杂度,用以调节统计区域合并(SRM)算法的分割尺度。此外,该文还将SRM改进为动态合并方式,并扩展到多波段的遥感影像。实验结果表明,该文提出的自适应尺度分割算法,比单一尺度下的分割精度更高。

In remote sensing image, there is significant difference between the scales of different objects, so any single-scale segmentation can barely produce satisfying result. This paper argues that appropriate segmentation scale can be selected according to the visual complexity of scene. Based on the Watson visual model, a method is proposed to calculate the complexity used for adapting the scale of the Statistical Region Merging （SRM）. In addition, the SRM is improved with dynamic merging mode and extended to multi-band image. The experiments demonstrate that the performance of the proposed adaptive scale segmentation is better than any single-scale segmentation