A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping (1806.02400v1)

Abstract: In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. Given a geographical region with fully mapped coffee plantations, we observe that this knowledge can be used to train a classifier and to map a new county with no need of samples indicated in the target region. Experimental results show that transferring knowledge via some UDA strategies performs better than just applying a classifier trained in a region to predict coffee crops in a new one. However, UDA methods may lead to negative transfer, which may indicate that domains are too different that transferring knowledge is not appropriate. We also verify that normalization affect significantly some UDA methods; we observe a meaningful complementary contribution between coffee crops data; and a visual behavior suggests an existent of a cluster of samples that are more likely to be drawn from a specific data.

• The paper presents a comparative study showing that unsupervised domain adaptation (UDA) methods generally outperform direct knowledge transfer for coffee crop mapping across different regions.
• Effective data normalization significantly impacts UDA performance, with methods like TCA showing strong results but needing careful preprocessing to avoid negative transfer.
• Using UDA can reduce the need for extensive ground-truth labeling in coffee crop mapping, but further research is needed to better manage domain shifts and prevent negative transfer.

Unsupervised Domain Adaptation for Coffee Crop Mapping

This paper presents a comparative paper on the application of existing unsupervised domain adaptation (UDA) techniques to the task of coffee crop mapping, particularly focusing on the context of transferring knowledge between geographic regions with different coffee growth patterns. The intent is to evaluate the efficacy of UDA methods in leveraging information from a mapped geographic region (source domain) to predict coffee crop presence in a new region (target domain) without the need for labeled samples from the target domain.

Experimental Findings

Extensive experiments were performed on a Brazilian Coffee Crops dataset comprising remote sensing images from four regions, each considered a distinct domain: Arceburgo, Guaxupé, Guaranésia, and Monte Santo. The paper involves several UDA methods, including Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Joint Distribution Adaptation (JDA) among others. The experiments aimed to answer key research questions such as the effectiveness of UDA over direct transfer, impact of data normalization, and complementarity of different datasets when used as sources.

One central finding is that UDA methods generally outperform direct knowledge transfer (transfer without adaptation) in mapping coffee crops. Notably, TCA with an L2-norm followed by Z-score normalization presented the best average performance across different source-target combinations. However, the paper also identifies situations of negative transfer, where the application of certain UDA techniques worsens performance compared to no adaptation, indicating substantial domain divergence.

Another critical observation is the importance of normalization. Different normalization techniques significantly impact UDA performance, underlining the necessity of careful preprocessing in practical applications. The paper highlights that while TCA offers robust transfer capabilities with appropriate preprocessing, results are not uniformly superior across all domain pairs.

Complementarity and Visual Analysis

The paper explores the complementarity between different datasets by examining prediction overlaps through Venn diagrams, revealing domain-specific advantages. Interestingly, certain source regions provide unique predictive insights that others do not, suggesting the presence of complementary information.

Moreover, a visual analysis employing dimensionality reduction techniques like t-SNE reveals clustering patterns among correctly classified samples, further supporting the notion of spatial and spectral similarity cues being shared between the source and target data.

Practical and Theoretical Implications

The paper's implications are twofold. Practically, it demonstrates how UDA can enhance crop mapping tasks by reducing reliance on extensive ground-truth labeling, which is often resource-intensive. Theoretically, it raises questions about handling domain shifts and improving UDA approaches to mitigate negative transfer more effectively.

Future Directions

Future work is suggested to focus on strategies to preempt negative transfer, possibly by developing methods to assess domain similarity prior to adaptation. Additionally, extending UDA frameworks to other types of agricultural mapping could unlock further applications of remote sensing technology in precision agriculture.

This paper significantly contributes to the domain of remote sensing and agroinformatics by providing insights into UDA's capabilities and limitations, particularly in the context of challenging agricultural datasets with variable spectral and spatial characteristics.