Using Classic Networks for Classifying Remote Sensing Images: Comparative Study

Al-Afandy, Khalid A.; Lazaar, Mohamed; Achhab, Mohammed Al

doi:10.25046/aj050594

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…This section illustrates the proposed methods' experiments setup and results and compares these proposed methods with previous methods' results in [20] and [21]. This comparison is established according to the OA measurements for each model.…”

Section: The Experimental Setup and Resultsmentioning

confidence: 99%

“…Fig. 10 offers the comparison based on the OA calculations for the proposed model that utilizes the FC layer and other models mentioned in [20] with both datasets.…”

Section: The Experimental Resultsmentioning

confidence: 99%

“…They utilized dense connections to build a small aggregate of convolution kernels to realize many reusable feature maps. AlAfandy et al [20] compared four remote sensing images' classifiers that utilized four classic networks; the VGG 16, the ResNet 50, the NASNet Mobile, and the DenseNet 169 models. Transfer learning was done for these networks, and then fully connected (FC) layers were created and retrained.…”

Section: Introductionmentioning

confidence: 99%

“…The SIRI-WHU dataset and the UC Merced land use dataset are utilized for training the proposed models in this paper. There are two steps to assess the efficiency of these proposals; the first step is comparing the experimental results of these proposed methods with the antecedent results in [20,21]. This comparative is done by measuring the overall accuracy (OA) for each classifier.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM

Al-Afandy¹,

El‐Sayed²,

Baz³

et al. 2022

Computers, Materials &Amp; Continua

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Remote sensing image processing engaged researchers' attentiveness in recent years, especially classification. The main problem in classification is the ratio of the correct predictions after training. Feature extraction is the foremost important step to build high-performance image classifiers. The convolution neural networks can extract images' features that significantly improve the image classifiers' accuracy. This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels' outputs as a features extraction using two classic convolution models; these convolution models are the ResNet 50 and the DenseNet 169. These elicited features have been used by the fully connected neural network classifier and support vector machine classifier as input features. The results of the proposed methods are compared with other antecedent approaches in the same experimental environments. Evaluation is based on learning curves plotted during the training of the proposed classifier that is based on a fully connected neural network and measuring the overall accuracy for the both proposed classifiers. The proposed classifiers are used with their trained weights to predict a big remote sensing scene's classes for a developed test. Experimental results ensure that, compared with the other traditional classifiers, the proposed classifiers are further accurate.

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Section: The Experimental Setup and Resultsmentioning

confidence: 99%

“…Fig. 10 offers the comparison based on the OA calculations for the proposed model that utilizes the FC layer and other models mentioned in [20] with both datasets.…”

Section: The Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM

Al-Afandy¹,

El‐Sayed²,

Baz³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…The models are evaluated using dermoscopy images taken from the HAM10000 dataset. The proposed model is compared with all the ResNet models [35,36] i.e., Resnet18, ResNet50 and ResNet101 in terms of precision, sensitivity, accuracy and F1 Score. Adadelta [37] optimizer is used for evaluation of models using 32 batch size.…”

Section: Experimental Setup and Performance Analysismentioning

confidence: 99%

Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

Anand¹,

Gupta²,

Koundal³

et al. 2022

Computers, Materials &Amp; Continua

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Biomedical image analysis has been exploited considerably by recent technology involvements, carrying about a pattern shift towards 'automation' and 'error free diagnosis' classification methods with markedly improved accurate diagnosis productivity and cost effectiveness. This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images. The proposed model has four convolutional layers, two maxpool layers, one fully connected layer and three dense layers. All the convolutional layers are using the kernel size of 3 * 3 whereas the maxpool layer is using the kernel size of 2 * 2. The dermoscopy images are taken from the HAM10000 dataset. The proposed model is compared with the three different models of ResNet that are ResNet18, ResNet50 and ResNet101. The models are simulated with 32 batch size and Adadelta optimizer. The proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90, the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as 0.86. Therefore, features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease classes. This model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

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