ABSRTACT: Aiming at the problems of high time cost, labor cost and low efficiency in traditional remote sensing image processing, and aiming at improving the processing speed, accuracy and reducing the number of parameters in the classification of remote sensing hyperspectral images (HSI), an improved 2DCNN model En-De-2CP-2DCNN is proposed.Firstly, 1DCNN, 2DCNN and 3DCNN were used to carry out classification experiments on the Pavia University HSI dataset, and the advantages and disadvantages of each were compared and analyzed.Secondly, under the premise of maintaining fast processing speed and not increasing the number of model parameters, 2DCNN is selected as the basic model, referring to the Encode-Decode structure of SegNet, and integrating the idea of double convolutional pooling to improve the basic model and optimize the learning strategy.The results show that the accuracy F1-score of the En-De-2CP-2DCNN model is 99.36%, which reaches the same level as 3DCNN (99.21%), which is 2.11% higher than that before the improvement (97.25%). The processing speed (7s/epoch) and 1DCNN are in the same order of magnitude, faster than 3DCNN (100s/epoch); The amount of parameters (3.66 MB) is higher than 3DCNN (318 KB), but much lower than 1DCNN (19.3 MB).It basically realizes the goal of accurate, fast and lightweight remote sensing hyperspectral image processing, and provides technical support for the automatic processing of remote sensing images.In particular, the improvement in processing speed and parameter amount is conducive to further realizing the lightweight deployment of mobile terminals..