What Kind Of Spatial And Spectral Resolution Of Uav-Borne Hyperspectral Image Is Required For Precise Crop Classification When Using Deep Learning

Bin, Yang; Hu, Shunshi

doi:10.1109/whispers56178.2022.9955102

Cited by 1 publication

(1 citation statement)

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“…Studies [61,62] used the WHU-Hi dataset to evaluate their methods using high spectral and very high spatial resolution imagery. In the first study, the authors examined the impact of spatial and spectral resolution on classification precision.…”

Section: Crop Classification Using Uav Datamentioning

confidence: 99%

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%