Semantic Segmentation Based on Temporal Features: Learning of Temporal–Spatial Information From Time-Series SAR Images for Paddy Rice Mapping

Synthetic aperture radar (SAR) can be used to obtain remote sensing images of different growth stages of crops under all weather conditions. Such time-series SAR images can provide an abundance of temporal and spatial features for use in large-scale crop mapping and analysis. In this study, we propose a temporal feature-based segmentation (TFBS) model for accurate crop mapping using time-series SAR images. This model first extracts deep-seated temporal features and then learns the spatial context of the extracted temporal features for crop mapping. The results indicate that the TFBS model significantly outperforms traditional long short-term memory (LSTM), U-network, and convolutional LSTM models in crop mapping based on time-series SAR images. TFBS demonstrates better generalizability than other models in the study area, which makes it more transferable, and the results show that data augmentation can significantly improve this generalizability. The visualization of the temporal features extracted by the TFBS shows that there is a high degree of intraclass homogeneity among rice fields and interclass heterogeneity between rice fields and other features. TFBS also achieved the highest accuracy of the four deep learning models for multicrop classification in the study area. This study presents a feasible way of producing high-accuracy large-scale crop maps based on the proposed model.

I. Introduction

World production has grown significantly over the past 60 years, which has greatly reduced the proportion of hungry and undernourished people in the world. Nevertheless, a new set of challenges threatens world food security [1]–[4]. The number of hungry people worldwide has slowly risen since 2014, and a recent estimate for 2019 has revealed that an additional 60 million people have become affected by hunger during the past five years [4]. Cropland monitoring plays an important role in understanding the state of food security [5], [6], providing critical basic information for analyzing the change in the crop growing area [7], fluctuation of yield [8], and formulation of agricultural policies [9].