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Different from object counting in surveillance scenes, remote sensing object counting encounters knotty challenges due to its tiny scale and cluttered background. However, existing remote sensing counting methods pursue favorable performance by sacrificing resolution to obtain semantic information, resulting in the loss of significant features of tiny-scale objects. To surmount the above issue, we propose a novel hierarchical kernel interaction network, dubbed HKINet, for remote sensing object counting. HKINet is comprised of several hierarchical kernel interaction modules (HKIMs) to simultaneously preserve high-resolution features and extract deep-layer semantic information. Specifically speaking, HKIM hierarchically performs multiresolution convolutions to avoid information loss in low resolution. Moreover, a scale interaction block (SIB) is used to combine multiresolution features for semantic information interaction. Finally, hierarchical resolutions are fused to output the prediction density map. To validate the superiority of our proposed HKINet, we conduct extensive experiments on four remote sensing object counting datasets, e.g., RSOC, CARPK, PUCPR+, and DroneCrowd datasets, and experimental results demonstrate HKINet outperforms other state-of-the-art remote sensing counting methods in terms of mean absolute error (MAE) and root mean squared error (RMSE).