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Abstract: Artificial neural networks (ANNs) have made great strides in the field of remote sensing image object detection. However, low detection efficiency and high power consumption have always been a significant bottleneck in remote sensing. spiking neural networks (SNNs) process information in the form of sparse spikes, creating the advantage of high energy efficiency for computer vision tasks. However, most work has focused on simple classification tasks, and only a few researchers have applied SNNs to object detection in natural images. In this study, we consider the parsimonious nature of biological brains and propose a fast ANN-to-SNN conversion method for remote sensing image detection. We establish a fast sparse model for pulse sequence perception based on group sparse features and conduct transform-domain sparse resampling of the original image to enable fast perception of image features and encoded pulse sequences. In addition, to meet accuracy requirements in relevant remote sensing scenarios, we analyze the transformation error theoretically and propose channel self-decaying weighted normalization to elimi-nate neuron overactivation. We propose S3Det, a remote sensing image object detection model. Our experiments, based on a large publicly available remote sensing dataset, show that S3Det achieves an accuracy performance similar to that of the ANN. Meanwhile, our transformed network is only 24% sparser than the benchmark and consumes only 1.46 J. On the simulation platform, our algorithm improves the integrated inference time 48 times more than the benchmark and consumes a fraction (1/122) of the original algorithm's power.