Sugarcane crop classification using time series analysis of optical and SAR sentinel images: a deep learning approach

Sreedhar, Rahul; Varshney, Avnish; Madhu, Dhanya

doi:10.1080/2150704x.2022.2088254

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…Authors found that a DL approach called pixel-set encoder-temporal-attention encoder (PSE-TAE) algorithm outperformed classical approaches like RF. They also found that their method for data fusion enabled the training of models that performed better than using only Sentinel-1 or Sentinel-2 data, which is in line with previous studies using data fusion [23,24,43].…”

Section: Crop Classification Using Satellite Datasupporting

confidence: 85%

“…Furthermore, the results showed that the kernel depth in the 3D-convolution operator had a significant impact on the performance of the 3D-CNN. LSTM models, Sentinel-1 and Sentinel-2 data were utilized in studies [42,43]. The first study proposes an approach for improving classification accuracy in mountainous areas with rain and clouds.…”

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

“…The network was tested in a region of Taiwan, and the precision, recall, and overall accuracy metrics were used to evaluate the results. The study found that the ESO network outperformed the use of LSTM models, Sentinel-1 and Sentinel-2 data were utilized in studies [42,43]. The first study proposes an approach for improving classification accuracy in mountainous areas with rain and clouds.…”

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

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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 Satellite Datasupporting

confidence: 85%

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

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

et al. 2023

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“…In the transportation system, the logistics path-planning performance is insufficient and there is the problem of long computation times, so Yu et al proposed the deep reinforcement learning mechanism to optimize the logistics path-planning model to verify the model’s effectiveness, and found that the computation time of the model compared with the traditional model was significantly shortened, and the phase of the computation time in the path planning was better [ 15 ]. To further improve the precision and accuracy of fluid flow control, Rabault et al proposed to apply deep reinforcement learning techniques to the training of the flow control to construct an active control model of the flow and validated the effectiveness of this model, which was found to successfully stabilize the vortex channel and reduce the resistance by approximately 8%, opening the way for the execution of active flow control [ 16 , 17 , 18 ]. In order to improve the elasticity of a power system, Sreedhar and others used a deep reinforcement learning algorithm to build a power system data-driven agent framework, whereby the validity of the method can achieve accurate system model calculations, overcome scalability problems, and enhance the deployment of power system elastic shunt planning [ 19 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy

Liu,

Zhang,

Lee

et al. 2024

Biomimetics

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The current motion interaction model has the problems of insufficient motion fidelity and lack of self-adaptation to complex environments. To address this problem, this study proposed to construct a human motion control model based on the muscle force model and stage particle swarm, and based on this, this study utilized the deep deterministic gradient strategy algorithm to construct a motion interaction control model based on the muscle force model and the deep reinforcement strategy. Empirical analysis of the human motion control model proposed in this study revealed that the joint trajectory correlation and muscle activity correlation of the model were higher than those of other comparative models, and its joint trajectory correlation was up to 0.90, and its muscle activity correlation was up to 0.84. In addition, this study validated the effectiveness of the motion interaction control model using the depth reinforcement strategy and found that in the mixed-obstacle environment, the model’s desired results were obtained by training 1.1 × 103 times, and the walking distance was 423 m, which was better than other models. In summary, the proposed motor interaction control model using the muscle force model and deep reinforcement strategy has higher motion fidelity and can realize autonomous decision making and adaptive control in the face of complex environments. It can provide a theoretical reference for improving the effect of motion control and realizing intelligent motion interaction.

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“…Expanding beyond maritime applications, SAR-optical data have been used for classification in other domains like land cover, agriculture, etc. The studies by Shakya et al [14] and Sreedhar et al [15] demonstrated the broader utility of SAR-optical data fusion in areas like land cover and agriculture. Shakya et al emphasized gradient-based data fusion for classification, while Sreedhar et al highlighted the combined use of SAR's all-weather imaging and the multispectral capabilities of optical datasets for time series analysis in crop classification.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

Tienin,

Cui,

Mba Esidang

et al. 2023

Remote Sensing

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The classification of ship images has become a significant area of research within the remote sensing community due to its potential applications in maritime security, traffic monitoring, and environmental protection. Traditional monitoring methods like the Automated Identification System (AIS) and the Constant False Alarm Rate (CFAR) have their limitations, such as challenges with sea clutter and the problem of ships turning off their transponders. Additionally, classifying ship images in remote sensing is a complex task due to the spatial arrangement of geospatial objects, complex backgrounds, and the resolution limitations of sensor platforms. To address these challenges, this paper introduces a novel approach that leverages a unique dataset termed Heterogeneous Ship data and a new technique called the Spatial–Channel Attention with Bilinear Pooling Network (SCABPNet). First, we introduce the Heterogeneous Ship data, which combines Synthetic Aperture Radar (SAR) and optical satellite imagery, to leverage the complementary features of the SAR and optical modalities, thereby providing a richer and more-diverse set of features for ship classification. Second, we designed a custom layer, called the Spatial–Channel Attention with Bilinear Pooling (SCABP) layer. This layer sequentially applies the spatial attention, channel attention, and bilinear pooling techniques to enhance the feature representation by focusing on extracting informative and discriminative features from input feature maps, then classify them. Finally, we integrated the SCABP layer into a deep neural network to create a novel model named the SCABPNet model, which is used to classify images in the proposed Heterogeneous Ship data. Our experiments showed that the SCABPNet model demonstrated superior performance, surpassing the results of several state-of-the-art deep learning models. SCABPNet achieved an accuracy of 97.67% on the proposed Heterogeneous Ship dataset during testing. This performance underscores SCABPNet’s capability to focus on ship-specific features while suppressing background noise and feature redundancy. We invite researchers to explore and build upon our work.

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Sugarcane crop classification using time series analysis of optical and SAR sentinel images: a deep learning approach

Cited by 13 publications

References 14 publications

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

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

Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

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