On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network

Minallah, Nasru; Tariq, Mohsin; Aziz, Najam; Khan, Waleed; Rehman, Atiq Ur; Belhaouari, Samir Brahim

doi:10.1371/journal.pone.0239746

Cited by 24 publications

(7 citation statements)

References 23 publications

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“…Bilinear interpolation is a technique used to shift digital images to various sampling rasters. After that, the normalized difference vegetation index (NDVI) was calculated (Equation ) (Minallah et al., 2020).

\begin{equation} NDVI = \frac{NIR - RL}{NIR + RL} ,\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

Classification of tobacco using remote sensing and deep learning techniques

Qazi,

Ahmad,

Minallah

et al. 2023

Agronomy Journal

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Tobacco is an important crop in many countries, and its management could be improved by accurate yield predictions. Traditional yield estimation methods like human‐based surveys are inaccurate, time consuming, and expensive. In this work, we consider the problem of tobacco identification and classification from satellite imagery and propose a Conv1D and long short‐term memory (LSTM) based deep learning model. We compare the performance of our proposed Conv1D and LSTM deep learning model with benchmark machine learning models, namely support vector machine, random forest, and LSTM. Our model had an accuracy of 98.4%. The use of accurate models can improve the decision process.

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\begin{equation} NDVI = \frac{NIR - RL}{NIR + RL} ,\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

Classification of tobacco using remote sensing and deep learning techniques

Qazi,

Ahmad,

Minallah

et al. 2023

Agronomy Journal

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“…Convolutional neural networks are a class of ANNs that has proven their effectiveness in the area of image recognition and classification. Recently, deep CNNs [18] are making inroads in other areas and are performing well. Vrskova et al, [19] performed a hyper-parameter case study on in buildings vegetation detection using CNN.…”

Section: Related Workmentioning

confidence: 99%

Effect of hyper-parameters on the performance of ConvLSTM based deep neural network in crop classification

et al. 2023

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Deep learning based data driven methods with multi-sensors spectro-temporal data are widely used for pattern identification and land-cover classification in remote sensing domain. However, adjusting the right tuning for the deep learning models is extremely important as different parameter setting can alter the performance of the model. In our research work, we have evaluated the performance of Convolutional Long Short-Term Memory (ConvLSTM) and deep learning techniques, over various hyper-parameters setting for an imbalanced dataset and the one with highest performance is utilized for land-cover classification. The parameters that are considered for experimentation are; Batch size, Number of Layers in ConvLSTM model, and No of filters in each layer of the ConvLSTM are the parameters that will be considered for our experimentation. Experiments also have been conducted on LSTM model for comparison using the same hyper-parameters. It has been found that the two layered ConvLSTM model having 16-filters and a batch size of 128 outperforms other setting scenarios, with an overall validation accuracy of 97.71%. The accuracy achieved for the LSTM is 93.9% for training and 92.7% for testing.

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“…Ground truth data from the study area was collected at the end of September 2020 using an locally designed geo-survey mobile application [12]. The amount of data collected during the ground survey of all the classes considered for this study is shown in Table 1.…”

Section: Data Preparationmentioning

confidence: 99%

Hyperparameters analysis of long short-term memory architecture for crop classification

Sher

Minallah

Ahmad

et al. 2023

IJECE

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<span lang="EN-US">Deep learning (DL) has seen a massive rise in popularity for remote sensing (RS) based applications over the past few years. However, the performance of DL algorithms is dependent on the optimization of various hyperparameters since the hyperparameters have a huge impact on the performance of deep neural networks. The impact of hyperparameters on the accuracy and reliability of DL models is a significant area for investigation. In this study, the grid Search algorithm is used for hyperparameters optimization of long short-term memory (LSTM) network for the RS-based classification. The hyperparameters considered for this study are, optimizer, activation function, batch size, and the number of LSTM layers. In this study, over 1,000 hyperparameter sets are evaluated and the result of all the sets are analyzed to see the effects of various combinations of hyperparameters as well the individual parameter effect on the performance of the LSTM model. The performance of the LSTM model is evaluated using the performance metric of minimum loss and average loss and it was found that classification can be highly affected by the choice of optimizer; however, other parameters such as the number of LSTM layers have less influence.</span>

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On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network

Cited by 24 publications

References 23 publications

Classification of tobacco using remote sensing and deep learning techniques

Classification of tobacco using remote sensing and deep learning techniques

Effect of hyper-parameters on the performance of ConvLSTM based deep neural network in crop classification

Hyperparameters analysis of long short-term memory architecture for crop classification

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