Remote Sensing Image Segmentation Method Research Articles

Multi-scale and Multi-feature Segmentation of High Resolution Remote Sensing Image

With the development of the remote sensing technology, high resolution remote sensing images widely penetrates into the common people's life. Traditional medium or low resolution image processing method based on pixel doesn't meet people's requirement any more. In view of it, this paper puts forward a high resolution remote sensing image segmentation method based on the traditional watershed algorithm with multiple scales and characteristics and introduces two new concepts-wrong waterlogging basin and merging array. It firstly improves the immersion process of traditional algorithm, then optimizes the wrong water logging basin and the merging array of basin, adopts the eight immersion neighbors avoiding the wrong division based on four neighbors immersion, optimizes the multilayer immersion process, segments the high resolution remote sensing image with different scales and different features, and finally combines the similar regions. The simulation experiments show that the improved watershed algorithm not only keeps the accurate segmentation effect of traditional algorithm, but also has much higher efficiency.

High-resolution remote sensing image segmentation based on improved RIU-LBP and SRM

In this paper, we propose an improved rotation invariant uniform local binary pattern (RIU-LBP) operator for segmenting high-resolution sensing image which can effectively describe the texture features of a high-resolution remote sensing image. The improved RIU-LBP is based on RIU-LBP. It introduces a threshold in binarization of region pixels. The new LBP operator can better tolerate small texture variation and better distinguish the plain and rough texture than the original RIU-LBP does. Then, a merging criterion of texture regions is proposed, which is based on regional LBP value distribution and Bhattacharyya distance. Finally, the texture merging criterion and spectral merging criterion are combined in the statistical region merging (SRM)-based remote sensing image segmentation method to improve segmentation results, taking full advantage of rich spectral and texture information in high-resolution remote sensing images. This algorithm can be adjusted to the number of segmented regions, and experiments indicate better segmentation results than ENVI 5.0 and the SRM method.