Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy

Mo, Wang; Wang, Jing; Cui, Yunpeng; Liu, Juan; Chen, Li

doi:10.3390/agronomy12102342

Cited by 13 publications

(5 citation statements)

References 45 publications

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“…The patch-based method involves classifying crops by inputting patches of a certain size around the pixels to be classified into a deep network. The object-based classification method based on voting is a crop classification approach proposed by Wang et al [50], which involves using random forest for pixel-level classification and then determining the category of the cropland field parcels based on the proportion of each category under cropland field parcels coverage. In the comparative experiment, the random forest model also utilized a random grid search to determine parameters, while the deep learning model was set according to the default parameters.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

Tian,

Chen,

et al. 2023

Agronomy

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Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

Tian,

Chen,

et al. 2023

Agronomy

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“…Sentinel-1 data are not affected by cloud cover, but are significantly affected by the noise signal. Therefore, the "median" function was used to synthesize the monthly median value of VH to reduce the noise in the data [15]. Due to the difference in data acquisition time between Sentinel-1 and Sentinel-2, the monthly synthesis of VH can also ensure that the time was neat with Sentinel-2 data.…”

Section: Constructing the Feature Datasetmentioning

confidence: 99%

“…There are two ways to extract crop structure information, statistical survey and remote sensing extraction [11]. Compared to statistical survey, remote sensing extraction has the advantages of wide coverage, strong timeliness and low cost, so remote sensing extraction has become the main method of extracting plant structure in a wide range of crops [12][13][14][15][16][17]. As far back as 1991, researchers utilized Landsat TM multi-temporal data to classify surface vegetation cover types, demonstrating that the application of multi-temporal classification yields greater accuracy compared to single-temporal classification [12].…”

Section: Introductionmentioning

confidence: 99%

Using Time Series Sentinel Images for Object-Oriented Crop Extraction of Planting Structure in the Google Earth Engine

Zhang,

Ying,

et al. 2023

Agronomy

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Timely and accurate extraction of crop planting structure information is of great importance for food security and sustainable agricultural development. However, long time series data with high spatial resolution have a much larger data volume, which seriously limits the quality and efficiency of the application of remote sensing to agriculture in complex crop rotation areas. To address this problem, this paper takes Lujiang County, a typical complex crop rotation region in the middle and lower reaches of the Yangtze River in China as an example, and proposes utilizing the Google Earth Engine (GEE) platform to extract the Normalized Difference Vegetation Index (NDVI), Normalized Difference Yellowness Index (NDYI) and Vertical-Horizontal Polarization (VH) time series sets of the whole planting year, and combining the Simple Non-Iterative Clustering (SNIC) multi-scale segmentation with the Support Vector Machine (SVM) and Random Forest (RF) algorithms to realize the fast and high-quality planting information of the main crop rotation patterns in the complex rotation region. The results show that by combining time series and object-oriented methods, SVM leads to better improvement than RF, with its overall accuracy and Kappa coefficient increasing by 4.44% and 0.0612, respectively, but RF is more suitable for extracting the planting structure in complex crop rotation areas. The RF algorithm combined with time series object-oriented extraction (OB + T + RF) achieved the highest accuracy, with an overall accuracy and Kappa coefficient of 98.93% and 0.9854, respectively. When compared to the pixel-oriented approach combined with the Support Vector Machine algorithm based on multi-temporal data (PB + M + SVM), the proposed method effectively reduces the presence of salt-and-pepper noise in the results, resulting in an improvement of 6.14% in overall accuracy and 0.0846 in Kappa coefficient. The research results can provide a new idea and a reliable reference method for obtaining crop planting structure information efficiently and accurately in complex crop rotation areas.

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“…Advancements in computing resources have led to recent developments in deep learning methods, enabling not only pixel classification but also paddy field detection and segmentation tasks. Semantic segmentation models like U-Net can classify pixel categories of paddy fields [31,32]. Instance segmentation models, such as Mask RCNN, retain the information of individual objects, making them a feasible solution to detect paddy fields from remote sensing images.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approach for Paddy Field Detection Using Labeled Aerial Images: The Case of Detecting and Staging Paddy Fields in Central and Southern Taiwan

Chou

2023

Remote Sensing

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Detecting and mapping paddy fields in Taiwan’s agriculture is crucial for managing agricultural production, predicting yields, and assessing damages. Although researchers at the Taiwan Agricultural Research Institute currently use site surveys to identify rice planting areas, this method is time-consuming. This study aimed to determine the optimal band combinations and vegetation index for accurately detecting paddy fields during various phenological stages. Additionally, the Mask RCNN instance segmentation model in the ArcGIS Pro software was employed to enhance the effectiveness of detecting and segmenting paddy fields in aerial images. This study utilized aerial images collected from 2018 to 2019 covering Changhua, Yunlin, Chiayi, and Tainan in central and southern Taiwan, with a label file comprising four categories of rice growing, ripening, harvested stage, and other crops. To create different image datasets, the image pre-processing stage involved modifying band information using different vegetation indices, including NDVI, CMFI, DVI, RVI, and GRVI. The resolution of the training image chips was cropped to 550 by 550 pixels. After the model training process, the study found that the ResNet-50 backbone performed better than the ResNet-101, and the RGB + DVI image dataset achieved the highest mean average precision of 74.01%. In addition, the model trained on the RGB + CMFI image dataset was recommended for detecting paddy fields in the rice growing stage, RGB + NIR for the rice ripening stage, and RGB + GRVI for the rice harvested stage. These models exhibit Dice coefficients of 79.59%, 89.71%, and 87.94%, respectively. The detection and segmentation results can improve the efficiency of rice production management by using different band combinations according to different rice phenological stages. Furthermore, this method can be applied to large-scale detection of other crops, improving land use survey efficiency and reducing the burden on researchers.

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Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy

Cited by 13 publications

References 45 publications

Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

Using Time Series Sentinel Images for Object-Oriented Crop Extraction of Planting Structure in the Google Earth Engine

Deep Learning Approach for Paddy Field Detection Using Labeled Aerial Images: The Case of Detecting and Staging Paddy Fields in Central and Southern Taiwan

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