Uninterrupted optical image time series are crucial for the timely monitoring
of agricultural land changes. However, the continuity of such time series is
often disrupted by clouds. In response to this challenge, we propose a deep
learning method that integrates cloud-free optical (Sentinel-2) observations
and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data, using
a combined Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN)
architecture to generate continuous Normalized Difference Vegetation Index
(NDVI) time series. We emphasize the significance of observation continuity by
assessing the impact of the generated time series on the detection of grassland
mowing events. We focus on Lithuania, a country characterized by extensive
cloud coverage, and compare our approach with alternative interpolation
techniques (i.e., linear, Akima, quadratic). Our method surpasses these
techniques, with an average MAE of 0.024 and R^2 of 0.92. It not only improves
the accuracy of event detection tasks by employing a continuous time series,
but also effectively filters out sudden shifts and noise originating from
cloudy observations that cloud masks often fail to detect.

利用深度学习方法，结合免云的光学观测和天气无关的合成孔径雷达数据，采用组合卷积神经网络 (CNN) - 循环神经网络 (RNN) 架构生成连续的归一化植被指数 (NDVI) 时间序列，该方法在连续观测序列中对事件检测任务的准确性进行了改进，并有效滤除由云观测引起的突变和噪声。

利用深度学习进行云空缺填补，改善草地监测

Cloud gap-filling with deep learning for improved grassland monitoring

Selective weed treatment is a critical step in autonomous crop management as
related to crop health and yield. However, a key challenge is reliable, and
accurate weed detection to minimize damage to surrounding plants. In this
paper, we present an approach for dense semantic weed classification with
multispectral images collected by a micro aerial vehicle (MAV). We use the
recently developed encoder-decoder cascaded Convolutional Neural Network (CNN),
Segnet, that infers dense semantic classes while allowing any number of input
image channels and class balancing with our sugar beet and weed datasets. To
obtain training datasets, we established an experimental field with varying
herbicide levels resulting in field plots containing only either crop or weed,
enabling us to use the Normalized Difference Vegetation Index (NDVI) as a
distinguishable feature for automatic ground truth generation. We train 6
models with different numbers of input channels and condition (fine-tune) it to
achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification
metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested
for MAV integration. Dataset used in this paper is released to support the
community and future work.

本文介绍了一种使用编码器 - 解码器串联卷积神经网络进行密集语义杂草分类的方法，以通过微型飞行器收集的多光谱图像减少损害周围植物的可靠而精确的杂草检测，训练不同数量通道的模型并在嵌入式 GPU 系统中进行部署。