Deep learning models have achieved remarkable results in remote sensing image classification. With the continuous collection of new remote sensing images， when the remote sensing image classification models based on deep learning train new data to learn new knowledge， their recognition performance of old data will decline， that is， old knowledge forgetting. In order to help remote sensing image classification model consolidate old knowledge and learn new knowledge， a remote sensing image classification model based on sample incremental learning， namely ICLKM （Incremental Collaborative Learning Knowledge Model） was proposed. The model consisted of two knowledge networks. The first network mitigated knowledge forgetting by retaining the output of the old model through knowledge distillation. The second network took the output of new data as the learning objective of the first network and effectively learned new knowledge by maintaining the consistency of the dual network models. Finally， two networks learned together to generate more accurate model through knowledge collaboration strategy. Experimental results on two remote sensing datasets NWPU-RESISC45 and AID show that， ICLKM has the accuracy improved by 3.53 and 6.70 percentage points respectively compared with FT （Fine-Tuning） method. It can be seen that ICLKM can effectively solve the knowledge forgetting problem of remote sensing image classification and continuously improve the recognition accuracy of known remote sensing images.