Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture.

Huang, Huasheng; Tang, Yu; Tan, Zhiping; Zhuang, Jiajun; Hou, Chaojun; Chen, Weizhao; Ren, Jinchang

Abstract

Color calibration is a critical step for unmanned aerial vehicle (UAV) remote sensing, especially in precision agriculture, which relies mainly on correlating color changes to specific quality attributes, e.g. plant health, disease, and pest stresses. In UAV remote sensing, the exemplar-based color transfer is popularly used for color calibration, where the automatic search for the semantic correspondences is the key to ensuring the color transfer accuracy. However, the existing attention mechanisms encounter difficulties in building the precise semantic correspondences between the reference image and the target one, in which the normalized cross correlation is often computed for feature reassembling. As a result, the color transfer accuracy is inevitably decreased by the disturbance from the semantically unrelated pixels, leading to semantic mismatch due to the absence of semantic correspondences. In this article, we proposed an unsupervised object-based attention mechanism (OBAM) to suppress the disturbance of the semantically unrelated pixels, along with a further introduced weight-adjusted Adaptive Instance Normalization (AdaIN) (WAA) method to tackle the challenges caused by the absence of semantic correspondences. By embedding the proposed modules into a photorealistic style transfer method with progressive stylization, the color transfer accuracy can be improved while better preserving the structural details. We evaluated our approach on the UAV data of different crop types including rice, beans, and cotton. Extensive experiments demonstrate that our proposed method outperforms several state-of-the-art methods. As our approach requires no annotated labels, it can be easily embedded into the off-the-shelf color transfer approaches. Relevant codes and configurations will be available at https://github.com/huanghsheng/object-based-attention-mechanism

Citation

HUANG, H., TANG, Y., TAN, Z., ZHUANG, J., HOU, C., CHEN, W. and REN, J. 2022. Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture. IEEE transactions on geoscience and remote sensing [online], 60, article number 4416013. Available from: https://doi.org/10.1109/TGRS.2022.3224580

• Journal Article Type: Article
• Acceptance Date: Nov 24, 2022
• Online Publication Date: Nov 24, 2022
• Publication Date: Dec 31, 2022
• Deposit Date: Jan 20, 2023
• Publicly Available Date: Jan 20, 2023
• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 60
• Article Number: 4416013
• DOI: https://doi.org/10.1109/TGRS.2022.3224580
• Keywords: Unmanned aerial vehicles (UAV); Semantic correspondences; Attention mechanism; Color transfer
• Public URL: https://rgu-repository.worktribe.com/output/1818252