To address the problems of high cost of time and labor and low efficiency frustrated traditional remote sensing image processing,an improved 2DCNN (2D Convolutional Neural Network) model abbreviated as En-De-2CP-2DCNN was proposed,with the purpose to improve the processing speed,accuracy and reduce the number of parameters in the classification of remote sensing Hyperspectral Images (HSI).First,1DCNN,2DCNN and 3DCNN were used to carry out classification experiments on Pavia University HSI dataset,and their advantages and disadvantages were compared and analyzed.Second,under the premise of maintaining fast processing speed without increasing model parameters,the 2DCNN was selected as the basic model,which was then improved with referring to the Encoder-Decoder structure of SegNet and integrating the idea of double convolutional pooling,and the learning strategy was optimized.The results show that the F1-score of the proposed En-De-2CP-2DCNN model is 99.96％,reaching the same level of 3DCNN (99.36％),which is 2.68 percentage points higher than that before improvement (97.28％);the processing speed (5 s／epoch) is comparable to that of 1DCNN and faster than 3DCNN (96 s／epoch);the amount of parameters is reduced from 3.55 MB to 2.01 MB,which is higher than 3DCNN (316 KB) but much lower than 1DCNN (19.21 MB).The proposed En-De-2CP-2DCNN model realizes accurate,fast and lightweight processing of remote sensing hyperspectral images.In particular,the improvement in processing speed and parameter amount is conducive to further realizing the lightweight deployment of mobile terminals.