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The deep learning method is widely used in remote sensing object detection on the premise that the training data have complete features. However, when data with a fixed class are added continuously, the trained detector is less able to adapt to new instances, impelling it to carry out incremental learning (IL). IL has two tasks with knowledge-related symmetry: continuing to learn unknown knowledge and maintaining existing knowledge. Unknown knowledge is more likely to exist in these new instances, which have features dissimilar from those of the old instances and cannot be well adapted by the detector before IL. Discarding all the old instances leads to the catastrophic forgetting of existing knowledge, which can be alleviated by relearning old instances, while different subsets represent different existing knowledge ranges and have different memory-retention effects on IL. Due to the different IL values of the data, the existing methods without appropriate distinguishing treatment preclude the efficient absorption of useful knowledge. Therefore, a rank-aware instance-incremental learning (RAIIL) method is proposed in this article, which pays attention to the difference in learning values from the aspects of the data-learning order and training loss weight. Specifically, RAIIL first designs the rank-score according to inference results and the true labels to determine the learning order and then weights the training loss according to the rank-score to balance the learning contribution. Comparative and analytical experiments conducted on two public remote sensing datasets for object detection, DOTA and DIOR, verified the superiority and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]