Enhanced Pixel Based Urban Area Classification of Satellite Images Using Convolutional Neural Network

Laban, Noureldin; Abdellatif, Bassam; Moushier, Hala; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.21608/ijicis.2021.79070.1099

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Yang et al [35] performed novel leak detection in oil and gas pipeline systems. Laban et al [36] performed urban area classification in satellite images. The rough peanuts resulted in signal differences, so the target objects in hyperspectral images were averaged to obtain one-dimensional data.…”

Section: D-cnnmentioning

confidence: 99%

The Development of Peanut Defect Detection Technique Using Hyperspectral Images

Chen¹,

Kuo²,

Zhang³

et al. 2023

Preprint

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In Taiwan, peanuts are classified by hand. The kernel quality is judged based on the appearance of peanuts. The identification takes considerable time and labor. Fatigue can induce misrecogni-tion or grade cognition instability due. Peanut defects are generally divided into health, underde-velopment, insect bite, and rupture. This study employed two imaging methods push-boom FX10 and Snapshot. Deep learning was used to detect peanut defects. A push-broom instrument was used for testing. There were 1,560 peanuts, including 240 good and 240 bad ones. There were a total of 1,080 good peanuts and bad peanuts in the test set data, the band selection was used, and a CNN model was built. Based on the results from three methods, 3DCNN could classify with 97%accuracy. The Snapshot was used to achieve the real-time design of a lightweight CNN model. Finally, five bands were selected using PCA, and the screening could be accurate and efficient. The maximum overall accuracy was about 98.5%. Kappa was 97.3% with real-time recognition. This will be a major advantage in the future practical application and commercialization processes. This technique can reduce labor costs, achieve intelligent detection and intelligent grading, and bring breakthroughs to smart agriculture for other crops as well.

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Section: D-cnnmentioning

confidence: 99%

The Development of Peanut Defect Detection Technique Using Hyperspectral Images

Chen¹,

Kuo²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the last years, simultaneously with improvements in the computational power of GPUs, methodologies based on deep learning are mainly characterized as one of the most powerful approaches in image processing, such as [6][7][8][9][10]. Image segmentation has been a hot topic since its emergence and since then many researchers have worked on the detection and segmentation of melanoma.…”

Section: Related Workmentioning

confidence: 99%

Experimental Comparative Study on Autoencoder Performance for Aided Melanoma Skin Disease Recognition

Diame

ElBery

Salem³

et al. 2022

IJICIS

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Melanoma is a dangerous and metastatic cancer that may be fatal and it has a high ability to invade other tissues and organs. Early diagnosis is an important reason to recover from melanoma and reduce mortality. So, automatic skin segmentation is considered an enthusiastic study at present. In this paper, we investigate the applicability of deep learning approaches to the segmentation of skin lesions by evaluating five architectures: Deeplabv3plus, mobilenetv2_unet, Resnet50_unet, vgg19_unet by providing a comparative study of those methods. All methods were trained on the ISIC2017 dataset. The methods were trained on the original dataset, and then the dataset was pre-processed for use in training the five methods. We used quantitative evaluation metrics to evaluate the performance of the methods. The Deeplabv3+ architecture showed significant results compared to the rest of the architecture in F1 as high as 89%, Jaccard as high as 83% and Recall as high as 91%.

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“…CNNs may include a nonlinear gating function i.e. Rectified Linear Unit (ReLU) to keep only the positive values [20].…”

Section: Proposed Network Architecturementioning

confidence: 99%

Hyperspectral Image Analysis Using a Custom Spectral Convolutional Neural Network

ElBery¹,

Salem²,

Moushier³

et al. 2022

IJICIS

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In recent time, the most applied classification method for hyperspectral images is based on the supervised deep learning approach. The hyperspectral images require special handling while it consists of hundreds of bands / channels. In this article, the experiments are conducted using one of the widespread deep learning models, Convolutional Neural Networks (CNNs), specifically, Csutom Spectral CNN architecture (CSCNN). The introduced network is based on the data reduction and data normalization. It firstly ommits the unnecessary data channels and retains the meaningful ones. Then, it passes the remaining data through the CNN layers (convolutional, rectified linear unit, fully connected, dropout,…etc) until reaches the classification layer. The experiments are applied on four benchmarcks [hyperspectral datasets], namely, Salinas-A, Kenndy Space Center (KSC), Indian Pines (IP), and Pavia University (Pavia-U). The proposed model achieved an overall accuracy more than 99.50 %. In last, a comparison versus the state of the art is also introduced.

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Enhanced Pixel Based Urban Area Classification of Satellite Images Using Convolutional Neural Network

Cited by 6 publications

References 19 publications

The Development of Peanut Defect Detection Technique Using Hyperspectral Images

The Development of Peanut Defect Detection Technique Using Hyperspectral Images

Experimental Comparative Study on Autoencoder Performance for Aided Melanoma Skin Disease Recognition

Hyperspectral Image Analysis Using a Custom Spectral Convolutional Neural Network

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