Deep learning-based models for temporal satellite data processing: Classification of paddy transplanted fields

Rawat, Anuvi; Kumar, Anil; Upadhyay, Priyadarshi; Kumar, Shashi

doi:10.1016/j.ecoinf.2021.101214

Cited by 18 publications

(9 citation statements)

References 39 publications

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“…Time series data has become the main data type for crop classification. This paper conducts experiments and shows that time series data is more effective in rice extraction than a single image, and this conclusion has been confirmed in many studies [11,14,62]. Selecting appropriate time intervals is the key to construct time series data.…”

Section: The Choice Of Time Interval and Band Combinationsupporting

confidence: 54%

“…The launch of Landsat 8 provides more satellite remote sensing (RS) data and makes crop type identification and finer resolution mapping realistic [9,10]. Landsat data have been widely used for agricultural studies as it provides almost 40 years of data archives, is freely available, and is easier for visualization purposes [3,[11][12][13][14]. Several researchers have worked on temporal Landsat data to monitor growing dynamics for paddy rice and other crop classification.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Huang

Wei

et al. 2022

Agronomy

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Timely, accurate, and repeatable crop mapping is vital for food security. Rice is one of the important food crops. Efficient and timely rice mapping would provide critical support for rice yield and production prediction as well as food security. The development of remote sensing (RS) satellite monitoring technology provides an opportunity for agricultural modernization applications and has become an important method to extract rice. This paper evaluated how a semantic segmentation model U-net that used time series Landsat images and Cropland Data Layer (CDL) performed when applied to extractions of paddy rice in Arkansas. Classifiers were trained based on time series images from 2017–2019, then were transferred to corresponding images in 2020 to obtain resultant maps. The extraction outputs were compared to those produced by Random Forest (RF). The results showed that U-net outperformed RF in most scenarios. The best scenario was when the time resolution of the data composite was fourteen day. The band combination including red band, near-infrared band, and Swir-1 band showed notably better performance than the six widely used bands for extracting rice. This study found a relatively high overall accuracy of 0.92 for extracting rice with training samples including five years from 2015 to 2019. Finally, we generated dynamic maps of rice in 2020. Rice could be identified in the heading stage (two months before maturing) with an overall accuracy of 0.86 on July 23. Accuracy gradually increased with the date of the mapping date. On September 17, overall accuracy was 0.92. There was a significant linear relationship (slope = 0.9, r2 = 0.75) between the mapped areas on July 23 and those from the statistical reports. Dynamic mapping is not only essential to assist farms and governments for growth monitoring and production assessment in the growing season, but also to support mitigation and disaster response strategies in the different growth stages of rice.

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Section: The Choice Of Time Interval and Band Combinationsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Huang

Wei

et al. 2022

Agronomy

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“…Despite having the ability to extract a single class, FCM has a disadvantage of coincident clusters, which was removed with the modified PCM (MPCM) algorithm. 24 As evident from previous studies, MPCM is an efficient soft classification algorithm that has worked well for LULC classification, 25 paddy, 9 wheat, 15 etc. The delineation of fields in the final classified image obtained as output has been observed as precise.…”

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confidence: 75%

Study of integrated optical and synthetic aperture radar-based temporal indices database for specific crop mapping using fuzzy machine learning model

Sabir

Kumar

2023

J. Appl. Rem. Sens.

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For specific crop mapping, temporal data is used to handle the spectral overlap between classes. But a number of times, availability of temporal optical remote sensing data is not consistent with the requirements. In this study, a medicinal crop, psyllium husk, was mapped in Jalore district of Rajasthan, India while trying to integrate optical temporal data with synthetic aperture radar (SAR) data. Vegetation indices derived from optical and SAR data, i.e., modified soil adjusted vegetation index 2 (MSAVI2) and radar vegetation index (RVI), respectively, were used to prepare temporal indices databases. Three different temporal layer combinations of MSAVI2 and RVI layers, corresponding to different phenological stages of the target crop, were studied to determine the efficacy of different sensors for crop mapping. The outputs were assessed on the basis of classification efficiency with mean membership difference and variance for determining the effect of heterogeneity in the mapped fields. The results were compared with those obtained using only optical temporal data. From the results obtained, it was observed that the introduction of SAR-based vegetation index as a temporal layer gave good results for fully vegetated and harvest stages of the target crop. The variance within mapped fields was observed as low as 0.0003.

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“…Figure 6 shows a LSTM cell. Where X(t), M(t-1), M(t), N(t-1), N(t), f, c, I, O, U and W are the input vector, the output from the prior cell, the output from the current cell, memory value from the values cell, forget gate including sigmoid activation function, the candidate date including tanh activation function, the input gate including sigmoid activation function, the output gate including sigmoid activation function, and the weight vectors for forget gate, candidate gate, input gate, and output gate, respectively (Rawat, Kumar, Upadhyay, Kumar, 2021). Figure 7 shows the proposed architecture for the CNN-LSTM network.…”

Section: Lstm Networkmentioning

confidence: 99%

“…Despite the fact that machine learning algorithms mentioned above are not able to fully extract the spectral and spatial features of interest features, deep learning algorithms (e.g., Convolutional Neural Networks, LeNet-5, LSTM, Autoencoder, Fusion in-Decoder) have been proposed as a means of improving mapping accuracy by extracting high-level features from low-level features in crop fields. (Zhao, Liu, Ding, Liu, Wu, Wu, 2020;Zhang, Lin, Wang, Sun, Fu, 2018;Guo, Jia, Paull, 2018;Jo, Lee, Park, Lim, Song, Lee, Lee, 2020;Zhang, Liu, Wu, Zhan, Wei, 2020;Zhao, Liu, Ding, Liu, Wu, Wu, 2020;Jiang, Liu, Wu, 2018;Rawat, Kumar, Upadhyay, Kumar, 2021, Fathi, Shah-Hosseini, 2021. Researchers have recently applied deep learning to map corn and soybean fields by analyzing spectral features extracted from Landsat-8 images using LSTM (Deep Crop Mapping) (Xu, Zhu, Zhong, Lin, Xu, Jiang, Lin, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automatic Corn and Soybean Mapping Based on Deep Learning Methods (Case Study: Hamilton, Hardin, Boone, Story, Dallas, Polk, and Jusper Counties in Lowa State)

Fathi

Shah-Hosseini²

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Corn and Soybean are important crops for the world’s people. Agricultural planning relies heavily on monitoring and mapping corn and soybean fields. With the development of remote sensing technology and deep learning algorithms, corn and soybean fields are being managed more intelligently nowadays. By using Landsat-8 images with multi-temporal maps of NDVI index, we intend to compare deep learning models such as 1-D CNN, 1-D CNN-LSTM, and 2-D U-net for separating corn and soybean fields from other crops (because soybean and corn fields have similar NDVI curves) in the United States in 2020. It was found that the 2-D U-net model performed the most accurately for corn and soybean classes with Kappa coefficients of 88.48 and 88.89, respectively. This can be explained by the identification of complex features using NDVI multi-temporal indexes of March to November in the United States.

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Deep learning-based models for temporal satellite data processing: Classification of paddy transplanted fields

Cited by 18 publications

References 39 publications

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Study of integrated optical and synthetic aperture radar-based temporal indices database for specific crop mapping using fuzzy machine learning model

Automatic Corn and Soybean Mapping Based on Deep Learning Methods (Case Study: Hamilton, Hardin, Boone, Story, Dallas, Polk, and Jusper Counties in Lowa State)

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