Enhancing Cross-Domain Early Crop Mapping with CropSTGAN: Bridging Large Dissimilarities in Data Distributions

Основные понятия

The CropSTGAN framework effectively transforms the target domain's spectral features to resemble those of the source domain, enabling accurate early crop mapping in the target domain without requiring ground truth labels.

Аннотация

The key highlights and insights from the content are:

1. The paper introduces the CropSTGAN framework to address the cross-domain challenges in early crop mapping, where the spectral features of crops exhibit significant inter-region and inter-annual variability.

2. CropSTGAN consists of three main components: a pre-processor, a CropSTGAN domain mapper, and a TempCNN crop mapper. The domain mapper learns to transform the target domain data to the source domain, effectively bridging large dissimilarities in data distributions.

3. Comprehensive experiments were conducted across various regions and years, benchmarking CropSTGAN against state-of-the-art methods like TempCNN and STDAN. CropSTGAN significantly outperformed these methods in scenarios with large data distribution dissimilarities between the target and source domains.

4. The CropSTGAN domain mapper employs a unique structure to capture the temporal and spectral features from the time-series multispectral images, enabling effective transformation of the target domain data to the source domain.

5. The proposed approach does not require ground truth labels from the target domain, making it practical for early crop mapping in regions with limited data availability.

Статистика

The average temperature in Jackson County ranges from 289.02K to 290.76K across the years.
The average hourly precipitation in Jackson County ranges from 1.06mm/h to 2.60mm/h across the years.
The average hourly evaporation in Jackson County ranges from -1.11mm/h to -1.01mm/h across the years.
The average surface net solar radiation in Jackson County ranges from 4688.12kJ/m^2 to 5245.53kJ/m^2 across the years.
The average temperature in the study area of China is 290.03K in 2019.
The average hourly precipitation in the study area of China is 1.99mm/h in 2019.
The average hourly evaporation in the study area of China is -1.99mm/h in 2019.
The average surface net solar radiation in the study area of China is 12542.54kJ/m^2 in 2019.

Цитаты

"CropSTGAN learns to transform the target domain's spectral features to those of the source domain, effectively bridging large dissimilarities."
"Comprehensive experiments across various regions and years demonstrate the benefits and effectiveness of the proposed approach."
"CropSTGAN significantly outperforms these methods in scenarios with large data distribution dissimilarities between the target and source domains."

Ключевые выводы из

Cross Domain Early Crop Mapping using CropSTGAN

by Yiqun Wang,H... в arxiv.org 04-22-2024

https://arxiv.org/pdf/2401.07398.pdf

Дополнительные вопросы

How can the CropSTGAN framework be extended to handle more than two domains, enabling cross-domain mapping across multiple regions and years

To extend the CropSTGAN framework to handle more than two domains for cross-domain mapping across multiple regions and years, a few modifications and enhancements can be implemented. One approach is to introduce additional generator and discriminator networks for each new domain. Each generator would be responsible for transforming the data from its respective domain to the source domain, while the discriminators would distinguish between real and transformed data for each domain. By expanding the framework in this manner, it can accommodate multiple domains and facilitate cross-domain mapping across various regions and years.

What other types of remote sensing data, beyond multispectral images, could be incorporated into the CropSTGAN framework to further improve its performance

Incorporating additional types of remote sensing data beyond multispectral images can enhance the performance of the CropSTGAN framework. For example, including hyperspectral data can provide more detailed spectral information, allowing for better differentiation between crop types and improving classification accuracy. LiDAR data can offer valuable insights into the 3D structure of crops, aiding in crop identification and mapping. Radar data can be utilized to assess soil moisture levels and crop health, complementing the information obtained from multispectral images. By integrating these diverse data sources, the CropSTGAN framework can achieve a more comprehensive and accurate analysis of crop cultivation areas.

How can the CropSTGAN framework be adapted to address the challenge of limited ground truth data in the source domain, in addition to the target domain

To address the challenge of limited ground truth data in the source domain, the CropSTGAN framework can be adapted by incorporating semi-supervised learning techniques. By leveraging a combination of labelled data from the source domain and unlabelled data from the target domain, the framework can semi-supervise the training process and improve model performance. Additionally, active learning strategies can be employed to iteratively select the most informative samples for labelling, optimizing the use of limited ground truth data. Transfer learning methods can also be utilized to transfer knowledge from related tasks with more abundant labelled data to enhance the model's performance in the source domain with limited ground truth. By integrating these approaches, the CropSTGAN framework can effectively address the challenge of limited ground truth data in both the source and target domains.