A classification model of land cover in high-resolution remote sensing images based on attention mechanism
A classification model of land cover based on attention mechanism is constructed in this paper to further promote the intelligent interpretation of land cover in high-resolution remote sensing images.The model can be used to enhance the feature representation of land cover classes by attention mechanism and improve the accuracy of classification.Eleven images from ZY-3 satellite are trained and six images from ZY-3 satellite are tested in Guanzhong and Southern regions in Shaanxi Province.The results show that the model is effective in the classification of housing and buildings,cultivated land and forests,and it is feasible in land cover classification of high-resolution remote sensing images.
deep learninghigh-resolutionland cover classificationattention mechanism
NETL
NSTL
万方数据
