The accurate mapping of crop production is crucial for ensuring food security, effective resource management, and sustainable agricultural practices. One way to achieve this is by analyzing high-resolution satellite imagery. Deep Learning has been successful in analyzing images, including remote sensing imagery. However, capturing intricate crop patterns is challenging due to their complexity and variability. In this paper, we propose a novel Deep learning approach that integrates HRNet with Separable Convolutional layers to capture spatial patterns and Self-attention to capture temporal patterns of the data. The HRNet model acts as a backbone and extracts high-resolution features from crop images. Spatially separable convolution in the shallow layers of the HRNet model captures intricate crop patterns more effectively while reducing the computational cost. The multi-head attention mechanism captures long-term temporal dependencies from the encoded vector representation of the images. Finally, a CNN decoder generates a crop map from the aggregated representation. Adaboost is used on top of this to further improve accuracy. The proposed algorithm achieves a high classification accuracy of 97.5\% and IoU of 55.2\% in generating crop maps. We evaluate the performance of our pipeline on the Zuericrop dataset and demonstrate that our results outperform state-of-the-art models such as U-Net++, ResNet50, VGG19, InceptionV3, DenseNet, and EfficientNet. This research showcases the potential of Deep Learning for Earth Observation Systems.

翻译：作物生产的精确测绘对于保障粮食安全、有效资源管理和可持续农业实践至关重要。实现这一目标的方法之一是分析高分辨率卫星影像。深度学习在图像分析（包括遥感影像）方面取得了成功。然而，由于作物模式的复杂性和变异性，捕捉精细的作物模式具有挑战性。本文提出了一种新颖的深度学习方法，该方法将HRNet与可分离卷积层相结合以捕捉空间模式，并利用自注意力机制捕捉数据的时间模式。HRNet模型作为骨干网络，从作物图像中提取高分辨率特征。HRNet模型浅层的空间可分离卷积能更有效地捕捉精细作物模式，同时降低计算成本。多头注意力机制从图像的编码向量表示中捕捉长期时间依赖关系。最后，CNN解码器从聚合表示中生成作物地图。在此基础上使用Adaboost进一步提升准确性。所提算法在生成作物地图时实现了97.5%的高分类准确率和55.2%的交并比。我们在Zuericrop数据集上评估了管线的性能，结果表明其结果优于U-Net++、ResNet50、VGG19、InceptionV3、DenseNet和EfficientNet等最先进模型。本研究展示了深度学习在地球观测系统中的潜力。