Impact of Sample Quality to Deep Learning Classification Model of Multiple Crop Types on UAV Remotely Sensed Images

Li, Xingang; Cui, Ying; Zhou, Xun; Liu, Suhong

doi:10.3233/atde220048

Cited by 1 publication

(2 citation statements)

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“…They also found that increasing the size of the training dataset enhanced the performance, as shown in Figure 6. In article [59], the authors explore the impact of sample quality on the accuracy of DL classification models for identifying multiple crop types. Two aspects of sample quality were investigated: the ratio of training and validation samples and the spatial resolution of the images.…”

Section: Crop Classification Using Uav Datamentioning

confidence: 99%

“…The study was conducted in North China and used three DL models (VGG16, VGG19, and ResNet50) to classify six types of crops. Different spatial resolutions In article [59], the authors explore the impact of sample quality on the accuracy of DL classification models for identifying multiple crop types. Two aspects of sample quality were investigated: the ratio of training and validation samples and the spatial resolution of the images.…”

Section: Crop Classification Using Uav Datamentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

et al. 2023

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In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

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Section: Crop Classification Using Uav Datamentioning

confidence: 99%

Section: Crop Classification Using Uav Datamentioning

confidence: 99%