Rule-based classification framework for remote sensing data

Elmannai, Hela; Salhi, Amina; Hamdi, Monia; Sliti, Mohamed; Algarni, Abeer D.; Loghmari, Mohamed Anis; Naceur, Mohamed Saber

doi:10.1117/1.jrs.13.014514

Cited by 5 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning has been widely used for remotely sensed image analysis and classification [30,31]. Support Vector Regression SVR model has demonstrated to be efficient in this particular research field.…”

Section: Support Vector Regression Modelmentioning

confidence: 99%

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Karray¹,

Elmannai²,

Toumi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Pedo-spectroscopy has the potential to provide valuable information about soil physical, chemical, and biological properties. Nowadays, we may predict soil properties using VNIR field imaging spectra (IS) such as Prisma satellite data or laboratory spectra (LS). The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) for the prediction of several soil properties, including clay, sand, silt, organic matter, nitrate NO3-, and calcium carbonate CaCO 3 , using five VNIR spectra dataset combinations (% IS, % LS) as follows: C1 (0% IS, 100% LS), C2 (20% IS, 80% LS), C3 (50% IS, 50% LS), C4 (80% IS, 20% LS) and C5 (100% IS, 0% LS). Soil samples were collected at bare soils and at the upper (0-30 cm) layer. The data set has been split into a training dataset 80% of the collected data (n = 248) and a validation dataset 20% of the collected data (n = 61). The proposed PLSR and SVR models were trained then tested for each dataset combination. According to our results, SVR outperforms PLSR for both: C1 (0% IS, 100% LS) and C5 (100% IS, 0% LS). For Soil Organic Matter (SOM) prediction, it achieves (R 2 = 0.79%, RMSE = 1.42%) and (R 2 = 0.76%, RMSE = 1.3%), respectively. The data fusion has improved the soil property prediction. The highest improvement was obtained for the SOM property (R 2 = 0.80%, RMSE = 1.39) when using the SVR model and applying the second Combination C2 (20% of IS and 80% LS).

show abstract

Section: Support Vector Regression Modelmentioning

confidence: 99%

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Karray¹,

Elmannai²,

Toumi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Recent research demonstrated that multiple DCNNs performs better than a single DCNN [3][4][5]. Thus, two DCNNs are trained independently and their feature vectors are combined by a concatenation and further processings to simplify the model complexity.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models Combining for Breast Cancer Histopathology Image Classification

Elmannai¹,

Hamdi²,

Algarni³

2021

IJCIS

Self Cite

View full text Add to dashboard Cite

Breast cancer is one of the foremost reasons of death among women in the world. It has the largest mortality rate compared to the types of cancer accounting for 1.9 million per year in 2020. An early diagnosis may increase the survival rates. To this end, automating the analysis and the diagnosis allows to improve the accuracy and to reduce processing time. However, analyzing breast imagery's is non-trivial and may lead to experts' disagreements. In this research, we focus on breast cancer histopathological images acquired using the microscopic scan of breast tissues. We present combined two deep convolutional neural networks (DCNNs) to extract distinguished image features using transfer learning. The pre-trained Inception and the Xceptions models are used in parallel. Then, the feature maps are combined and reduced by dropout before being fed to the last fully connected layers for classification. We follow a sub-image classification then a whole image classification based on majority vote and maximum probability rules. Four tissue malignancy levels are considered: normal, benign, in situ carcinoma, and invasive carcinoma. The experimentations are performed to the Breast Cancer Histology (BACH) dataset. The overall accuracy for the sub-image classification is 97.29% and for the carcinoma cases the sensitivity achieved 99.58%. The whole image classification overall accuracy reaches 100% by majority vote and 95% by maximum probability fusion decision. The numerical results showed that our proposed approach outperforms the previous methods in terms of accuracy and sensitivity. The proposed design allows an extension to whole-slide histology images classification.

show abstract

Bagging and Single Decision Tree Approaches to Dispersed Data

Przybyła–Kasperek

Aning

2021

Computational Science – ICCS 2021

View full text Add to dashboard Cite

Rule-based classification framework for remote sensing data

Cited by 5 publications

References 29 publications

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Deep Learning Models Combining for Breast Cancer Histopathology Image Classification

Bagging and Single Decision Tree Approaches to Dispersed Data

Contact Info

Product

Resources

About