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

Yang, Lingbo; Huang, Ran; Huang, Jingfeng; Lin, Tao; Wang, Limin; Mijiti, Ruzemaimaiti; Wei, Pengliang; Tang, Chao; Shao, Jie; Li, Qiangzi; Du, Xin

doi:10.1109/tgrs.2021.3099522

Cited by 19 publications

(17 citation statements)

References 73 publications

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“…Based on the random forest algorithm and Sentinel data, Fiorillo, et al [30] mapped lowland rice crop areas in the Sédhiou region (Senegal) from 2017 to 2019. However, the accuracy of these methods strongly depends on the number of training samples [31], which are difficult to obtain and update on a large scale [32].…”

Section: Introductionmentioning

confidence: 99%

High Resolution Distribution Dataset of Double-Season Paddy Rice in China

et al. 2021

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Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield.

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Section: Introductionmentioning

confidence: 99%

High Resolution Distribution Dataset of Double-Season Paddy Rice in China

et al. 2021

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“…Based on this design, the multi-scale features and spatial details information are well combined to generate the image segmentation map. Due to its excellent performance, UNet is no longer limited to the segmentation of medical images, and has been widely used in the field of remote sensing [24]- [26].…”

Section: Methodsmentioning

confidence: 99%

CroFuseNet: A Semantic Segmentation Network for Urban Impervious Surface Extraction Based on Cross Fusion of Optical and SAR Images

Guo

Shao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The fusion of optical and synthetic aperture radar (SAR) images is a promising method to extract urban impervious surface (IS) accurately. Previous studies have shown that the feature-level fusion of optical and SAR images can significantly improve IS extraction. However, they generally use simple layer stacking for features fusion, ignoring the interaction between optical and SAR images. Besides, most of the features they used are shallow features manually extracted, such as texture and geometric features, lacking the use of high-level semantic features of images. The lack of publicly available IS datasets is considered as an obstacle that prevents the extensive use of deep learning models in IS extraction. Therefore, this study first creates an open and accurate IS dataset based on optical and SAR images, and then proposes a semantic segmentation network based on cross fusion of optical and SAR images features, namely CroFuseNet, for IS extraction. In CroFuseNet, we design a cross fusion module (CFM) to fuse features of optical and SAR images to achieve better complementarity between the two types of images. And we propose a multimodal features aggregation (MFA) module to aggregate specific high-level features from optical and SAR images. To validate the proposed CroFuseNet, we compare it with two classical machine learning algorithms and four state-of-theart deep learning models. The proposed model has the highest accuracy, with OA, MIoU and F1-Score of 97.77%, 0.9495 and 0.9770 respectively. The quantitative and qualitative experimental results demonstrate that the proposed model is superior to these comparative algorithms.

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“…In addition, our model achieved reasonable pixel-level rice classification results, there is still room for improvements toward field recognition. Further improvements, include adopting object-based image analysis approaches [61,62] and combining CNN into the LSTM-MTL model [63,64], have the potential to generate the configuration of the crop field at large spatial scales.…”

Section: Rice Area Estimation Compared With CDLmentioning

confidence: 99%

Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series

et al. 2022

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Timely and accurate cropland information at large spatial scales can improve crop management and support the government in decision making. Mapping the spatial extent and distribution of crops on a large spatial scale is challenging work due to the spatial variability. A multi-task spatiotemporal deep learning model, named LSTM-MTL, was developed in this study for large-scale rice mapping by utilizing time-series Sentinel-1 SAR data. The model showed a reasonable rice classification accuracy in the major rice production areas of the U.S. (OA = 98.3%, F1 score = 0.804), even when it only utilized SAR data. The model learned region-specific and common features simultaneously, and yielded a significant improved performance compared with RF and AtBiLSTM in both global and local training scenarios. We found that the LSTM-MTL model achieved a regional F1 score up to 10% higher than both global and local baseline models. The results demonstrated that the consideration of spatial variability via LSTM-MTL approach yielded an improved crop classification performance at a large spatial scale. We analyzed the input-output relationship through gradient backpropagation and found that low VH value in the early period and high VH value in the latter period were critical for rice classification. The results of in-season analysis showed that the model was able to yield a high accuracy (F1 score = 0.746) two months before rice maturity. The integration between multi-task learning and multi-temporal deep learning approach provides a promising approach for crop mapping at large spatial scales.

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

Cited by 19 publications

References 73 publications

High Resolution Distribution Dataset of Double-Season Paddy Rice in China

High Resolution Distribution Dataset of Double-Season Paddy Rice in China

CroFuseNet: A Semantic Segmentation Network for Urban Impervious Surface Extraction Based on Cross Fusion of Optical and SAR Images

Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series

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