Crop Classification from UAV-Based Multi-spectral Images Using Deep Learning

Rao, Bimba; Hota, Manjit; Kumar, Uttam

doi:10.1007/978-981-16-1086-8_42

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The improved U-Net model was named ISDU-Net, and ISDU-Net was compared with the FCN, SegNet, and U-Net models to analyze the qualitative and quantitative results [51][52][53][54][55][56][57][58][59][60][61][62].…”

Section: Comparative Analysis Of Improved U-net and Classical Network...mentioning

confidence: 99%

Economic Fruit Forest Classification Based on Improved U-Net Model in UAV Multispectral Imagery

Jia

Yang

et al. 2023

Remote Sensing

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Economic fruit forest is an important part of Chinese agriculture with high economic value and ecological benefits. Using UAV multi-spectral images to research the classification of economic fruit forests based on deep learning is of great significance for accurately understanding the distribution and scale of fruit forests and the status quo of national economic fruit forest resources. Based on the multi-spectral remote sensing images of UAV, this paper constructed semantic segmentation data of economic fruit forests, conducted a comparative study on the classification and identification of economic fruit forests of FCN, SegNet, and U-Net classic semantic segmentation models, and proposed an improved ISDU-Net model. The recognition accuracies of the ISDU-Net were 87.73%, 70.68%, 78.69%, and 0.84, respectively, in terms of pixel accuracy, average intersection ratio, frequency weight intersection ratio, and Kappa coefficient, which were 3.19%, 8.90%, and 4.51% higher than the original U-Net model. The results showed that the improved ISDU-Net could effectively improve the learning ability of the model, perform better in the prediction of short sample categories, obtain a higher classification accuracy of fruit forest crops, and provide a new idea for the research on accurate fruit forest identification.

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Section: Comparative Analysis Of Improved U-net and Classical Network...mentioning

confidence: 99%

Economic Fruit Forest Classification Based on Improved U-Net Model in UAV Multispectral Imagery

Jia

Yang

et al. 2023

Remote Sensing

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“…At present, although UAVs have advantages in vegetation identification, such as the classification of crops, trees, and grass species (Garzon-Lopez and Lasso, 2020; Fan and Lu, 2021;Miura et al, 2021;Sudarshan Rao et al, 2021;Wicaksono and Hernina, 2021), the application of UAVs for infrastructure classification is still rare. Machine learning algorithms have not been widely used for infrastructure classification.…”

Section: Introductionmentioning

confidence: 99%

Nature-Based Solutions for Urban Water Management

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…At present, although UAVs have advantages in vegetation identification, such as the classification of crops, trees, and grass species (Garzon-Lopez and Lasso, 2020;Fan and Lu, 2021;Jiang et al, 2021;Miura et al, 2021;Sudarshan Rao et al, 2021;Wicaksono and Hernina, 2021), the application of UAVs for infrastructure classification is still rare. Machine learning algorithms have not been widely used for infrastructure classification.…”

Section: Introductionmentioning

confidence: 99%

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Jia

Cui

Liu

2022

Front. Environ. Sci.

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Green infrastructure (GI), such as green roofs, is now widely used in sustainable urban development. An accurate mapping of GI is important to provide surface parameterization for model development. However, the accuracy and precision of mapping GI is still a challenge in identifying GI at the small catchment scale. We proposed a framework for blue-green-gray infrastructure classification using machine learning algorithms and unmanned aerial vehicle (UAV) images that contained digital surface model (DSM) information. We used the campus of the Southern University of Science and Technology in Shenzhen, China, as a study case for our classification method. The UAV was a DJI Phantom 4 Multispectral, which measures the blue, green, red, red-edge, and near-infrared bands and DSM information. Six machine learning algorithms, i.e., fuzzy classifier, k-nearest neighbor classifier, Bayes classifier, classification and regression tree, support vector machine (SVM), and random forest (RF), were used to classify blue (including water), green (including green roofs, grass, trees (shrubs), bare land), and gray (including buildings, roads) infrastructure. The highest kappa coefficient was observed for RF and the lowest was observed for SVM, with coefficients of 0.807 and 0.381, respectively. We optimized the sampling method based on a chessboard grid and got the optimal sampling interval of 11.6 m to increase the classification efficiency. We also analyzed the effects of weather conditions, seasons, and different image layers, and found that images in overcast days or winter days could improve the classification accuracy. In particular, the DSM layer was crucial for distinguishing green roofs and grass, and buildings and roads. Our study demonstrates the feasibility of using UAV images in urban blue-green-gray infrastructure classification, and our infrastructure classification framework based on machine learning algorithms is effective. Our results could provide the basis for the future urban stormwater management model development and aid sustainable urban planning.

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Crop Classification from UAV-Based Multi-spectral Images Using Deep Learning

Cited by 4 publications

References 7 publications

Economic Fruit Forest Classification Based on Improved U-Net Model in UAV Multispectral Imagery

Economic Fruit Forest Classification Based on Improved U-Net Model in UAV Multispectral Imagery

Nature-Based Solutions for Urban Water Management

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

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