Accurate and timely crop type classification is essential for effective agricultural monitoring, cropland management, and yield estimation. Unfortunately, the complicated temporal patterns of different crops, combined with gaps and noise in satellite observations caused by clouds and rain, restrict crop classification accuracy, particularly during early seasons with limited temporal information. Although deep learning-based methods have exhibited great potential for improving crop type mapping, insufficient and noisy training data may lead them to overlook more generalizable features and derive inferior classification performance. To address these challenges, we developed a Mask Pixel-set SpatioTemporal Integration Network (Mask-PSTIN), which integrates a temporal random masking technique and a novel PSTIN model. Temporal random masking augments the training data by selectively removing certain temporal information to improve data variability, enforcing the model to learn more generalized features. The PSTIN, comprising a pixel-set aggregation encoder (PSAE) and long short-term memory (LSTM) module, effectively captures comprehensive spatiotemporal features from time-series satellite images. The effectiveness of Mask-PSTIN was evaluated across three regions with different landscapes and cropping systems. Results demonstrated that the addition of PSAE in PSTIN significantly improved crop classification accuracy compared to a basic LSTM, with average overall accuracy (OA) increasing from 80.9% to 83.9%, and the mean F1-Score (mF1) rising from 0.781 to 0.818. Incorporating temporal random masking in training led to further improvements, increasing average OA and mF1 to 87.4% and 0.865, respectively. The Mask-PSTIN significantly outperformed traditional machine learning and deep learning methods (i.e., RF, SVM, Transformer, and CNN-LSTM) in crop type mapping across all three regions. Furthermore, Mask-PSTIN enabled earlier and more accurate crop type identification before or during their developing stages compared to machine learning models. Feature importance analysis based on the gradient backpropagation algorithm revealed that Mask-PSTIN effectively leveraged multi-temporal features, exhibiting broader attention across various time steps and capturing critical crop phenological characteristics. These results suggest that Mask-PSTIN is a promising approach for improving both post-harvest and early-season crop type classification, with potential applications in agricultural management and monitoring.

中文翻译：

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通过将 LSTM 与时间随机掩码和像素集空间信息集成来改进裁剪类型映射


准确及时的作物类型分类对于有效的农业监测、农田管理和产量估算至关重要。不幸的是，不同作物复杂的时间模式，加上云和雨造成的卫星观测中的间隙和噪声，限制了作物分类的准确性，尤其是在时间信息有限的早期季节。尽管基于深度学习的方法在改进作物类型映射方面表现出了巨大的潜力，但训练数据不足和嘈杂可能会导致它们忽略更可概括的特征并得出较差的分类性能。为了应对这些挑战，我们开发了一个掩码像素集时空集成网络 （Mask-PSTIN），它集成了时间随机掩码技术和新颖的 PSTIN 模型。时间随机掩码通过选择性地删除某些时间信息来提高数据可变性，从而增强训练数据，从而强制模型学习更多通用特征。PSTIN由像素集聚合编码器（PSAE）和长短期记忆（LSTM）模块组成，可有效地从时间序列卫星图像中捕获全面的时空特征。在具有不同景观和种植系统的三个地区评估了 Mask-PSTIN 的有效性。结果表明，与基本 LSTM 相比，在 PSTIN 中添加 PSAE 显着提高了作物分类准确性，平均总体准确性 （OA） 从 80.9% 提高到 83.9%，平均 F1 分数 （mF1） 从 0.781 提高到 0.818。在训练中加入时间随机掩码导致了进一步的改进，平均 OA 和 mF1 分别提高到 87.4% 和 0.865。 在所有三个地区的作物类型映射中，Mask-PSTIN 的表现明显优于传统的机器学习和深度学习方法（即 RF、SVM、Transformer 和 CNN-LSTM）。此外，与机器学习模型相比，Mask-PSTIN 能够在作物类型之前或开发阶段更早、更准确地识别作物类型。基于梯度反向传播算法的特征重要性分析表明，Mask-PSTIN 有效地利用了多时间特征，在各个时间步长中表现出更广泛的关注并捕获了关键的作物物候特征。这些结果表明，Mask-PSTIN 是一种很有前途的方法，可以改进收获后和季节早期的作物类型分类，在农业管理和监测中具有潜在的应用。