Agricultural irrigation accounts for nearly 70% of global freshwater withdrawal. Among irrigation practices, contour-levee cascade irrigation is of particular interest as it is water-intensive and widely used in many rice production regions. Despite its significant environmental implications, no study has quantified the distribution of contour-levee irrigation. One major challenge of remote sensing-based contour-levee field detection is how to accurately identify the thin and curved levee lines whose appearance varies dramatically in different fields. This paper presents a new deep network-based method that jointly optimizes semantically meaningful features to quantify the contour-levee fields. This new method uses a bi-stream encoder-decoder architecture to capture spectral information and gradient features. To maintain image gradient sharpness, a skip connection approach is employed to facilitate gradient propagation across long-range connections. Moreover, the new method uses deep supervision to generate more informative features from the earlier hidden layers and superpixel segmentation to reduce classification noise as a post-processing step. By testing against 41 images across 10 Arkansas counties, the average accuracy was 86.23% and the method achieved 15%-17% improvement over benchmark methods. The results show that IrrNet-Bi-Seg maintains good transferability and is thus promising for larger-scale applications.

农业灌溉占全球淡水抽取量的近 70%。在灌溉实践中，等高堤梯级灌溉是特别令人感兴趣的，因为它是水资源密集型的，并且在许多水稻产区广泛使用。尽管具有重大的环境影响，但没有研究量化等高堤灌溉的分布。基于遥感的等高堤场检测的一大挑战是如何准确识别在不同场中外观差异显着的细而弯曲的堤线。本文提出了一种新的基于深度网络的方法，该方法联合优化语义上有意义的特征来量化轮廓堤防场。这种新方法使用双流编码器-解码器架构来捕获光谱信息和梯度特征。为了保持图像梯度锐度，采用跳跃连接方法来促进跨远程连接的梯度传播。此外，新方法使用深度监督从较早的隐藏层和超像素分割中生成更多信息特征，以减少分类噪声作为后处理步骤。通过对阿肯色州 10 个县的 41 张图像进行测试，平均准确率为 86.23%，该方法比基准方法提高了 15%-17%。结果表明，IrrNet-Bi-Seg 保持良好的可转移性，因此有望用于更大规模的应用。新方法使用深度监督从较早的隐藏层和超像素分割中生成更多信息特征，以减少分类噪声作为后处理步骤。通过对阿肯色州 10 个县的 41 张图像进行测试，平均准确率为 86.23%，该方法比基准方法提高了 15%-17%。结果表明，IrrNet-Bi-Seg 保持良好的可转移性，因此有望用于更大规模的应用。新方法使用深度监督从较早的隐藏层和超像素分割中生成更多信息特征，以减少分类噪声作为后处理步骤。通过对阿肯色州 10 个县的 41 张图像进行测试，平均准确率为 86.23%，该方法比基准方法提高了 15%-17%。结果表明，IrrNet-Bi-Seg 保持良好的可转移性，因此有望用于更大规模的应用。