AI-Based Crop Detection using Spectral Remote Sensing Images

提供免费的中/高特别分辨率卫星图像，使具有挑战性的动态土地使用和土地覆盖物制图问题有可能得到解决。该项目是关于开发用于作物检测和映射的新AI技术。作物检测是基于人工智能的时间序列分析的第一步，旨在为许多社会经济应用提供基础信息。例如作物控制和产量估计、变化监测、供应链和粮食安全、气候变化政策（如轮作、保险和施肥服务）。缺乏地面实况数据是作物检测的主要问题。这是大多数历史数据的时间序列分析的情况。另一方面，通过光谱卫星图像可以感知一年中特定作物在视觉和化学特性方面的变化。因此，该项目的重点是开发有效利用光谱波段进行作物检测的人工智能技术。这是基于（1）一个无监督的框架来实现的，以识别用于时间序列分析和作物检测的有效光谱带。为此，农作物指纹和其他植被指数被用来识别重要的光谱波长。(2)一个监督框架，用于开发一种新的光谱注意力模型，使用视觉变换预测策略。

The availability of free satellite images in medium/high special resolution has enabled possible solutions for the challenging dynamic land use and land cover mapping problems. This project is about developing new AI techniques for crop detection and mapping. Crop detection is the first step in AI-based time series analyses, aiming to provide fundamental information for many socio-economic applications. Examples are crop control and yield estimation, change monitoring, supply chain and food security, climate change policies such as crop rotation, insurance, and fertilization services.The lack of ground truth data is a major problem for crop detection. That is the case for most time-series analyses of historical data. On the other hand, the crop-specific variations in visual and chemical characteristics during a year are sensed by spectral satellite images. Therefore, this project is focused on developing AI techniques effectively utilizing the spectral bands for crop detection. This is achieved based on (1) an unsupervised framework to identify effective spectral bands for time-series analysis and crop detection. For this aim, the crops fingerprints and other vegetation indexes are used to identify the important spectral wavelengths. (2) a supervised framework for developing a novel spectral attention model using visual transformers prediction strategies.