Combining Deep Learning and Prior Knowledge for Crop Mapping in Tropical Regions from Multitemporal SAR Image Sequences

Abstract

Accurate crop type identification and crop area estimation from remote sensing data in tropical regions are still considered challenging tasks. The more favorable weather conditions, in comparison to the characteristic conditions of temperate regions, permit higher flexibility in land use, planning, and management, which implies complex crop dynamics. Moreover, the frequent cloud cover prevents the use of optical data during large periods of the year, making SAR data an attractive alternative for crop mapping in tropical regions. This paper evaluates the effectiveness of Deep Learning (DL) techniques for crop recognition from multi-date SAR images from tropical regions. Three DL strategies are investigated: autoencoders, convolutional neural networks, and fully-convolutional networks. The paper further proposes a post-classification technique to enforce prior knowledge about crop dynamics in the target area. Experiments conducted on a Sentinel-1 multitemporal sequence of a tropical region in Brazil reveal the pros and cons of the tested methods. In our experiments, the proposed crop dynamics model was able to correct up to 16.5% of classification errors and managed to improve the performance up to 3.2% and 8.7% in terms of overall accuracy and average F1-score, respectively.

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

“…In the next step, we will test whether the higher spatial resolution SAR data gaofen-3 (the highest spatial resolution is 1 m) combined with object-oriented method can significantly improve the accuracy of crop classification in small plot area. In addition, some studies have shown that band ratio or sentinel-1 radar vegetation index can better monitor agricultural land use [63] and other studies have proved that deep learning algorithm combined with SAR data can obtain higher crop classification accuracy [64], these are the directions that need further research.…”

On the Value of Sentinel-1 InSAR Coherence Time-Series for Vegetation Classification

“…It is for instant evident in the presence of yellow (sugarcane) pixels in areas where no sugarcane is expected. This issue could be partly mitigated by the application of a majority filter as a post-processing step [38]. The use of such a filter on areas with small and medium parcels (compared to the sensor resolution), such as the one shown in Figure 9, however, could be detrimental and its impact should be more carefully investigated in future works.…”

Rice Mapping Using a BiLSTM-Attention Model from Multitemporal Sentinel-1 Data

“…In the absence of a large amount of prior knowledge of rice, there will inevitably be some misclassification in the original classification results, so the original classification results need to be post-processed. Many researchers used post-processing methods to optimize the classification results [36,[61][62][63]. Therefore, we used FROM-GLC10 for the post-processing of rice extraction results, which reduced the false alarm to a certain extent.…”

“…Cué La Rosa et al combined FCNs with the Most Likely Class Sequence method and used 14 Sentinel-1 VV/VH polarization data to extract crops in tropical Brazil. The results revealed that FCNs tended to produce smoother results when compared with its counterparts [36]. Wei et al used the improved FCNs model U-Net and 18 Sentinel-1VV/VH data in 2017 to realize the crop classification in Fuyu City, Jilin Province, China [37].…”