Cross Domain Early Crop Mapping Using CropSTGAN

Driven by abundant satellite imagery, machine learning-based approaches have recently been promoted to generate high-resolution crop cultivation maps to support many agricultural applications. One of the major challenges faced by these approaches is the limited availability of ground truth labels. In the absence of ground truth, existing work usually adopts the “direct transfer strategy” that trains a classifier using historical labels collected from other regions and then applies the trained model to the target region. Unfortunately, the spectral features of crops exhibit inter-region and inter-annual variability due to changes in soil composition, climate conditions, and crop progress, the resultant models perform poorly on new and unseen regions or years. Despite recent efforts, such as the application of the deep adaptation neural network (DANN) model structure in the deep adaptation crop classification network (DACCN), to tackle the above cross-domain challenges, their effectiveness diminishes significantly when there is a large dissimilarity between the source and target regions. This paper introduces the Crop Mapping Spectral-temporal Generative Adversarial Neural Network (CropSTGAN), a novel solution for cross-domain challenges, that doesn’t require target domain labels. CropSTGAN learns to transform the target domain’s spectral features to those of the source domain, effectively bridging large dissimilarities. Additionally, it employs an identity loss to maintain the intrinsic local structure of the data. Comprehensive experiments across various regions and years demonstrate the benefits and effectiveness of the proposed approach. In experiments, CropSTGAN is benchmarked against various state-of-the-art (SOTA) methods. Notably, CropSTGAN significantly outperforms these methods in scenarios with large data distribution dissimilarities between the target and source domains.

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IEEE Access Article Awarded First Prize for Outstanding Research Articles in Biosurveillance

IEEE Access is extremely proud to announce that one of its published articles won first prize in the 2019 Awards for Outstanding Research Articles in Biosurveillance (Impact on Field of Biosurveillance Category) given by the International Society for Disease Surveillance. The article by Brenas, Al-Manir, Baker, & Shaban-Nejad entitled, “A Malaria Analytics Framework to Support Evolution and Interoperability of Global Health Surveillance Systems” was published in October 2017.

This prestigious award was presented by the International Society for Disease Surveillance, the premier organization dedicated to the advancement of the science and practice of biosurveillance. The award was created to recognize professionals and scientists of diseases surveillance for their outstanding contributions to this area of research. 

We congratulate these IEEE Access authors for their high-quality work, and thank them for choosing IEEE Access to publish their outstanding research. To read the full award-winning article, please click here.