Abstract:

【Objective】 Aiming to address the phenomena of low contrast, low saturation, and hue shift in unmanned aerial vehicle (UAV) photography images of forestland under hazy conditions, this study proposes a de-fogging method for UAV images adapted to forestland aerial photography scenes based on Resnet. 【Method】 The UAV images in woodland scenes were characterized by texture features and rich high-and low-frequency information. GFF information transfer modules were attached to each layer of the backbone network to transform feature maps into weight maps for filtering and sending to other layers, and thresholds were set at the receiving end to avoid the adverse effects of redundant information. Then, the global defogging effect was enhanced by dense links to improve the defogging quality in high- and low-frequency image regions. Finally, defogging experiments were conducted on a test set of woodland UAV images with fog. 【Result】 The average structural similarity of DHnet on the test set of woodland images was 0.83, and the average peak signal-to-noise ratio was 22.3 dB, which represented improvements of 4.8% and 39.3%, respectively, compared with the Resnet method. 【Conclusion】 The algorithm can effectively reduce tonal shift and remove residual fog, improving the color fidelity and detailed information retention of aerial woodland fog images obtained by UAV photography.

Key words: woodland, unmanned aerial vehicle(UAV), image dehaze, deep learning