Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

Li, Heping; Lu, Jiwen; Tian, Guixiang; Yang, Huige; Zhao, Jianhui; Li, Ning

doi:10.3390/rs14163889

Cited by 11 publications

(5 citation statements)

References 52 publications

(59 reference statements)

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“…The use of deep CNN with multi-spectral and temporal satellite images was pioneered by [ 44 ] for rice -crop mapping. Later, many studies used variations of CNN for PA tasks [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. In these patch-based studies, a large area of the land is extracted as a patch and thus requires further processing to extract relevant map of the crops.…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Khan

Gillani

Jamal

et al. 2023

Sensors

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Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

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Section: Introductionmentioning

confidence: 99%

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Khan

Gillani

Jamal

et al. 2023

Sensors

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“…CNNs have been used in several studies for the classification of land cover and groundwater potential zones, polarimetric SAR landcover classification [42], crop classification [43] and urban growth [44]. However, more recently, there have been different CNN techniques applied to the detection of various scenarios of water bodies and flood events globally.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data

Apan

Paudyal

et al. 2023

IJGI

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The accuracy of most SAR-based flood classification and segmentation derived from semi-automated algorithms is often limited due to complicated radar backscatter. However, deep learning techniques, now widely applied in image classifications, have demonstrated excellent potential for mapping complex scenes and improving flood mapping accuracy. Therefore, this study aims to compare the image classification accuracy of three convolutional neural network (CNN)-based encoder–decoders (i.e., U-Net, PSPNet and DeepLapV3) by leveraging the end-to-end ArcGIS Pro workflow. A specific objective of this method consists of labelling and training each CNN model separately on publicly available dual-polarised pre-flood data (i.e., Sentinel-1 and NovaSAR-1) based on the ResNet convolutional backbone via a transfer learning approach. The neural network results were evaluated using multiple model training trials, validation loss, training loss and confusion matrix from test datasets. During testing on the post-flood data, the results revealed that U-Net marginally outperformed the other models. In this study, the overall accuracy and F1-score reached 99% and 98% on the test data, respectively. Interestingly, the segmentation results showed less use of manual cleaning, thus encouraging the use of open-source image data for the rapid, accurate and continuous monitoring of floods using the CNN-based approach.

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“…This process is not only time-consuming and labor-intensive but also subject to subjective factors. Data updating is slow, which can greatly limit its practical application [5,6]. Furthermore, in most cases, the data obtained through traditional survey methods can only describe the rice-planting area through tabular digits and cannot provide an intuitive spatial distribution map of rice.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images

Tian

et al. 2023

Remote Sensing

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Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and accurately obtaining the rice mapping is crucial for ensuring global food security and mitigating global warming. In this study, we proposed an automated rice mapping method called automated rice mapping using V-shaped phenological features of rice (Auto-RMVPF) based on the time-series Sentinel-1A images, which are composed of four main steps. First, the dynamic threshold method automatically extracts abundant rice samples by flooding signals. Second, the second-order difference method automatically extracts the phenological period of rice based on the scattering feature of rice samples. Then, the key “V” feature of the VH backscatter time series, which rises before and after rice transplanting due to flooding, is used for rice mapping. Finally, the farmland mask is extracted to avoid interference from non-farmland features on the rice map, and the median filter is applied to remove noise from the rice map and obtain the final spatial distribution of rice. The results show that the Auto-RMVPF method not only can automatically obtain abundant rice samples but also can extract the accurate phenological period of rice. At the same time, the accuracy of rice mapping is also satisfactory, with an overall accuracy is more than 95% and an F1 score of over 0.91. The overall accuracy of the Auto-RMVPF method is improved by 2.8–12.2% compared with support vector machine (SVM) with an overall accuracy of 89.9% (25 training samples) and 92.2% (124 training samples), random forest (RF) with an overall accuracy of 82.8% (25 training samples) and 88.3% (124 training samples), and automated rice mapping using synthetic aperture radar flooding signals (ARM-SARFS) with an overall accuracy of 89.9%. Altogether, these experimental results suggest that the Auto-RMVPF method has broad prospects for automatic rice mapping, especially for mountainous regions where ground samples are often not easily accessible.

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Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

Cited by 11 publications

References 52 publications

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data

An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images

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