Automatic road extraction from High Resolution Remote Sensing Image is a challenging problem. In this paper we present a new approach for road automatically extraction which is based on topological derivative and mathematical morphology. This approach for road extraction can be divided into three main steps: using topological derivative for image segmentation, using mathematical morphology for road network identification and filtering. The experimental results show that this approach can effectively extract roads from high-resolution remote sensing image.
It is very important for the government to build an accurate national basic cultivated land database. In this work, farmland parcels extraction is one of the basic steps. However, during the past years, people had to spend much time on determining an area is a farmland parcel or not, since they were bounded to understand remote sensing images only from the mere visual interpretation. In order to overcome this problem, in this study, a method was proposed to extract farmland parcels by means of image classification. In the proposed method, farmland areas and ridge areas of the classification map are semantically processed independently and the results are fused together to form the final results of farmland parcels. Experiments on high spatial remote sensing images have shown the effectiveness of the proposed method.
It is difficult and boring for people to artificially extract farmland parcels from high resolution remote sensing images. Therefore, automatic methods are in the urgent need to release image interpreters from such a work as well as achieve accurate results. In the past years, although many researchers have made attempts to solve this problem by using different techniques and also produced some good results, they still cannot meet the demand of practical applications. In this paper, a farmland extraction method is proposed based on a new technique of two-stage image classification. The first stage aims at producing a map of farmland area by using the supervised iterative conditional mode (ICM), where a novel mixture posterior is proposed based on the tree-structured interpretation of certain complex landscapes, e.g., farmland and building area, and the Markov random field model (MRF) is also used to make use of spatial information between neighboring pixels. The second stage extracts the farmland parcels by using the Meanshift algorithm (MS) based on the hybrid of the original image and the texture image produced by the local binary pattern (LBP) method. We applied our method to a piece of aerial image in the urban area of Taizhou, China. The results show that the proposed method has an ability to produce more accurate results than the MS method.
In this paper, an algorithm of image segmentation using region-based MRF combined with boundary information is
proposed. Firstly we obtain the initial over segmentation regions by Meanshift (MS) algorithm and the regions that
include seed points are set to seed regions. Then, starting from the seed regions, the finally result is detected under the
framework of MRF, where an image model is built for the potential function of the regional MRF image segmentation
and combined with edge strength to define a suitable MRF potential function, which is based on the similarity criterion
of the value of the edge. The experimental results indicate that the algorithm can promote the adaptive capacity of the
MRF model and reserve more details as well as region homogeneous.