Automatically Extracting Information Form Maize Fields based on TM Remote Sensing Images

Many researches have focused on the automatic extraction of information from Landsat/TM remote sensing images.However,many of them seemed to be low effective,and the application of remote sensing technology is limited.Firstly,this paper analysed the current situation about how to extract arable land information from remote sensing images,and middle Jilin province and northeast Liaoning Province were selected as study area.The supervised classification method was adopted in the Landsat/TM image classification and maize land in the study area was extracted from the Landsat/TM images with the precision of up to 85.5%.Secondly,the method of automatic information extraction based on the multi-characters space in remote-sensing images was put forward.According to the method,maize fields information space was divided into several sub-spaces including spectral feature space,shape feature space,interference feature space and local geoscience-based feature space.Automatic classification of thematic information about maize land was carried out based on TM images and use of the method above.An expertise database was created for the automatic extraction of maize land from Landsat/TM images on the basis of multi-characters space with the use of knowledge engineer module from the ERDAS 8.5 software.The extraction of maize fields in the study area from the images was performed again on the basis of the expertise database.It was found that the interpretation was notably improved with the precision of 92.9%.Comparing this classification result with the traditional visual interpretation,it was concluded that the new method adopted in the paper could improve efficiency of thematic information extraction from the remote sensing images.At the same time,the method appears to have wide application perspective and have a great potential to be used in other areas.The method was also theoretically significant for automatic interpretation of remote-sensing images in the future.