Abstract Sunflower lodging is a common agricultural disorder taking place in the middle and late sunflower growth periods. This disorder reduces the sunflower seed yield, damages the seed quality, and hence usually causes great losses in both crop quantity and quality. Sunflower lodging is mainly caused by extreme and destructive weather events, which have been recently occurring more frequently. This is why it is highly crucial to develop methods for fast and accurate identification of sunflower lodging. In this work, an efficient method for sunflower lodging identification is proposed based on image fusion and deep semantic segmentation of remote sensing images obtained from an unmanned aerial vehicle (UAV). First, the resolution of low-resolution multispectral images was enhanced through matching their features with those of high-resolution visible-range images. Then, for effective lodging assessment, high-quality multispectral images with rich spectral information and high spatial resolution were obtained through fusing the visible-range images and the enhanced multispectral ones. Subsequently, in order to refine the identification outcomes, a variant of the segmentation network (SegNet) deep architecture was developed for semantic segmentation. This variant has skip connections, separable convolution, and a conditional random field. Experimental evaluation shows that the fusion-based approaches clearly outperform the no-fusion ones in terms of the lodging identification accuracy for all compared architectures including support vector machine (SVM), fully convolutional network (FCN), SegNet, and the proposed SegNet variant. Meanwhile, the deep semantic segmentation methods consistently outperform the classical SVM one with hand-crafted features. As well, the improved SegNet method outperformed all of the compared methods and achieved the best accuracies of 84.4% and 89.8% without and with image fusion, respectively, on one test. The corresponding accuracies on another test set were 76.6% and 83.3%, respectively. Moreover, the proposed method can also identify the sunflower lodging and non-lodging patterns and separate them from the background. These capabilities are highly beneficial for lodging hazard assessment and sunflower harvest survey. Overall, the proposed method effectively exploited UAV remote sensing image data with fusion and deep semantic segmentation modules in order to provide a useful reference for sunflower lodging assessment and mapping.

中文翻译：

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基于无人机遥感成像的图像融合和深度语义分割的向日葵倒伏识别

摘要 向日葵倒伏是向日葵生长期中后期常见的农业病害。这种紊乱会降低向日葵种子产量，损害种子质量，因此通常会造成作物数量和质量的巨大损失。向日葵倒伏主要是由极端和破坏性的天气事件引起的，这些事件最近发生得更加频繁。这就是为什么开发快速准确识别向日葵栖息地的方法非常重要的原因。在这项工作中，基于图像融合和从无人机（UAV）获得的遥感图像的深度语义分割，提出了一种有效的向日葵住宿识别方法。第一的，通过将低分辨率多光谱图像的特征与高分辨率可见范围图像的特征进行匹配，提高了低分辨率多光谱图像的分辨率。然后，为了进行有效的住宿评估，通过融合可见范围图像和增强的多光谱图像，获得具有丰富光谱信息和高空间分辨率的高质量多光谱图像。随后，为了细化识别结果，开发了一种用于语义分割的分割网络（SegNet）深度架构的变体。该变体具有跳过连接、可分离卷积和条件随机场。实验评估表明，对于包括支持向量机（SVM）在内的所有比较架构，基于融合的方法在住宿识别精度方面明显优于非融合方法，完全卷积网络 (FCN)、SegNet 和提议的 SegNet 变体。同时，深度语义分割方法始终优于具有手工制作特征的经典 SVM 方法。同样，改进的 SegNet 方法优于所有比较方法，在一次测试中，在没有图像融合和有图像融合的情况下分别达到了 84.4% 和 89.8% 的最佳准确率。在另一个测试集上的相应准确率分别为 76.6% 和 83.3%。此外，所提出的方法还可以识别向日葵的倒伏和非倒伏模式，并将它们与背景分开。这些功能对于住宿危害评估和向日葵收获调查非常有益。全面的，