Smoke Recognition in Satellite Imagery via an Attention Pyramid Network With Bidirectional Multilevel Multigranularity Feature Aggregation and Gated Fusion

Mingyuan Ren, Xiuwen Fu, Pasquale Pace, Gianluca Aloi, and Giancarlo FortinoRecognizing smoke in satellite imagery is a critical approach in an Internet of Things (IoT) system for monitoring forest fires. However, the task remains challenging due to false alarms of smoke-like occurrences caused by complex land cover types, and missing detections caused by the diversity of fire smoke. Some reasons are that existing methods overlook attention granularity, neglect all-layer-based fusion of low-level features with high-level semantic information, and fail to address interferences arising from fusing different kinds of features. To solve these issues, this paper presents an attention pyramid network with bidirectional multi-level multi-granularity feature aggregation and gated fusion for smoke recognition. First, to guide the model sequentially extract multi-granularity smoke attention clues for complementary smoke perception, we design an attention-guided feature pyramid module by concatenating residual blocks and attention pyramid blocks. Second, to leverage both low-level fine-grained and high-level semantic features in all network layers, we design a bidirectional feature aggregation module using multi-level multi-granularity feature blocks. Finally, to selectively integrate the features with different resolutions and semantic levels to effectively achieve feature complementarity and avoid feature mutual interference, we design a gated feature fusion module using gated feature fusion blocks. The experimental results demonstrate that our model achieves an accuracy of 98.33% on the USTC-SmokeRS dataset. Additionally, on the E-USTC-SmokeRS dataset, our model achieves a detection rate of 94.92%, a false alarm rate of 3.00%, and an F1-score of 0.9553. These results surpass the performance of existing satellite-imagery-based smoke recognition methods.