Joint sparse auto-encoder: A semi-supervised spatio-temporal approach in mapping large-scale croplands

TLDR: A novel learning framework to identify major crops without using labeled training samples for other land covers is proposed and the results confirm the effectiveness of spatial constraint in mitigating noise factors and making spatially contiguous classification.

Abstract: Automated cropland monitoring can offer timely and reliable agricultural information, which is critical to meet the increasing demand for food supply and food security. In most cropland mapping tasks, domain researchers provide manually labeled training samples for several major crop types and request for identifying these major crops in a target region. However, it is very expensive to hire experts to label all the other land covers that exist in the target region. In this paper, we propose a novel learning framework to identify major crops without using labeled training samples for other land covers. For each major crop type, we train a one-class classification model based on sparse-autoencoder (SAE). Specifically, we utilize the high-resolution (∼10m) remote sensing data as input features to classify each location either as one of major crop types or as other land covers. Many crop types are similar to each other in most dates of a year, but are distinguishable only during a short period in growing season. To better model the seasonal patterns of different crop types and to capture the their discriminative periods, we introduce a sliding window to cover different growing periods in a year and learn separate SAEs from these periods. Moreover, since remote sensing data are commonly disturbed by natural noise factors, we explore the spatial contiguity of unlabeled data in test region and incorporate it as a constraint in training process to further improve the performance. In this way, we utilize both labeled data and unlabeled data in a semi-supervised method to jointly train SAE. Finally, we design a mechanism to combine the SAEs trained for different crop types to make final classification decisions. We extensively evaluate the proposed method in mapping several major crops in Minnesota, US. The experimental results demonstrate that the proposed method can accurately map the extent of major crops, and capture the temporal growing patterns of different crops. Besides, the results confirm the effectiveness of spatial constraint in mitigating noise factors and making spatially contiguous classification. In addition, we give illustrative examples to show that the proposed method can help detect errors in existing cropland mapping product.