A comparison between Pixel-based deep learning and Object-based image analysis (OBIA) for individual detection of cabbage plants based on UAV Visible-light images

Ye, Zhangxi; Yang, Kaile; Lin, Yuwei; Guo, Shijie; Sun, Yiming; Chen, Xunlong; Lai, Riwen; Zhang, Houxi

doi:10.1016/j.compag.2023.107822

Cited by 31 publications

(21 citation statements)

References 48 publications

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“…The first approach is conducting OBIA to select and combine characteristics prior to inputting these data into a deep learning model for training [38,39], which was used in our study. The second approach implied employment of deep learning for initial image analysis, followed by merging the results with OBIA to enhance the ultimate recognition results [19,21]. In order to investigate more effective utilization of object-oriented approaches, we combined the two methods described above.…”

Section: Discussionmentioning

confidence: 99%

“…The integration of OBIA and deep learning can enhance the precision of semantic segmentation to a certain extent. This technique has been successfully applied in various applications, such as the extraction of vegetation [19], medical research [20], and the detection of buildings [21]. However, its application in identifying sloping cropland within a waterlogged area has not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Xie,

Wang,

Wang

et al. 2024

Sustainability

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Drainage difficulties in the waterlogged areas of sloping cropland not only impede crop development but also facilitate the formation of erosion gullies, resulting in significant soil and water loss. Investigating the distribution of these waterlogged areas is crucial for comprehending the erosion patterns of sloping cropland and preserving black soil resource. In this study, we built varied models based on two stages (one using only deep learning methods and the other combining object-based image analysis (OBIA) with deep learning methods) to identify waterlogged areas using high-resolution remote sensing data. The results showed that the five deep learning models using original remote sensing imagery achieved precision rates varying from 54.6% to 60.9%. Among these models, the DeepLabV3+-Xception model achieved the highest accuracy, as indicated by an F1-score of 53.4%. The identified imagery demonstrated a significant distinction in the two categories of waterlogged areas: sloping cropland erosion zones and erosion risk areas. The former had obvious borders and fewer misclassifications, exceeding the latter in terms of identification accuracy. Furthermore, the accuracy of the deep learning models was significantly improved when combined with object-oriented image analysis. The DeepLabV3+-MobileNetV2 model achieved the maximum accuracy, with an F1-score of 59%, which was 6% higher than that of the model using only original imagery. Moreover, this advancement mitigated issues related to boundary blurriness and image noise in the identification process. These results will provide scientific assistance in managing and reducing the impact in these places.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Xie,

Wang,

Wang

et al. 2024

Sustainability

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“…There are 12 index features in total and the formula of each index is listed in Table 1. (3) GLCM features are calculated from gray-level co-occurrence matrix (GLCM) and the total number of texture features are forty [4], including the mean, standard deviation, entropy, homogeneity, contrast, dissimilarity, angular second moment, and correlation of each band (B, G, R, RE, and NIR) in five directions (0 • , 45 • , 90 • , 135 • , and All).…”

Section: Feature Extraction For Each Objectmentioning

confidence: 99%

“…Traditional crop species identification mainly relies on manual ground surveys [3]. However, this method has drawbacks such as high cost, long time consumption, and difficulty in obtaining large-scale data [4]. In recent years, the rapid development of remote sensing technology has provided a new avenue for the accurate and rapid identification of crop species [5].…”

Section: Introductionmentioning

confidence: 99%

Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image

Deng,

Zhang,

Zheng

et al. 2024

Agriculture

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The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this study, we evaluated a methodology that integrates object-oriented method and random forest (RF) algorithm for crop identification using multispectral UAV images. The process involved a multiscale segmentation algorithm, utilizing the optimal segmentation scale determined by Estimation of Scale Parameter 2 (ESP2). Eight classification schemes (S1–S8) were then developed by incorporating index (INDE), textural (GLCM), and geometric (GEOM) features based on the spectrum (SPEC) features of segmented objects. The best-trained RF model was established through three steps: feature selection, parameter tuning, and model training. Subsequently, we determined the feature importance for different classification schemes and generated a prediction map of vegetation for the entire study area based on the best-trained RF model. Our results revealed that S5 (SPEC + GLCM + INDE) outperformed others, achieving an impressive overall accuracy (OA) and kappa coefficient of 92.76% and 0.92, respectively, whereas S4 (SPEC + GEOM) exhibited the lowest performance. Notably, geometric features negatively impacted classification accuracy, while the other three feature types positively contributed. The accuracy of ginger, luffa, and sweet potato was consistently lower across most schemes, likely due to their unique colors and shapes, posing challenges for effective discrimination based solely on spectrum, index, and texture features. Furthermore, our findings highlighted that the most crucial feature was the INDE feature, followed by SPEC and GLCM, with GEOM being the least significant. For the optimal scheme (S5), the top 20 most important features comprised 10 SPEC, 7 INDE, and 3 GLCM features. In summary, our proposed method, combining object-oriented and RF algorithms based on multispectral UAV images, demonstrated high classification accuracy for crops. This research provides valuable insights for the accurate identification of various crops, serving as a reference for future advancements in agricultural technology and crop management strategies.

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“…Our study holds significant utility, potentially reducing training data provision time by operators by 25-50%. We opt for the semi-automated approach of Object-Based Image Analysis (OBIA), leveraging its proficiency in delineating objects on very high-resolution orthophotos based on color proximity and object distance [28]. Furthermore, we enhance the annotations of OBIA results through morphology image processing to obtain optimal road annotations for effective integration into the model training process.…”

mentioning

confidence: 99%

Utilization of Improved Annotations From Object-Based Image Analysis as Training Data for DeepLab V3+ Model: A Focus on Road Extraction in Very High-Resolution Orthophotos

Sussi,

Husni,

Yusuf

et al. 2024

IEEE Access

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A comparison between Pixel-based deep learning and Object-based image analysis (OBIA) for individual detection of cabbage plants based on UAV Visible-light images

Cited by 31 publications

References 48 publications

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image

Utilization of Improved Annotations From Object-Based Image Analysis as Training Data for DeepLab V3+ Model: A Focus on Road Extraction in Very High-Resolution Orthophotos

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