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.
To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).