Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques

Karadal, Can Haktan; Kaya, M. Çağrı; Tuncer, Türker; Doğan, Şengül; Acharya, U. Rajendra

doi:10.1016/j.eswa.2021.115659

Cited by 44 publications

(16 citation statements)

References 55 publications

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“…This mapping allows the model to find unique patterns that give the detection model the ability to discern between an RSO and a star. 16,21 A feature of this model is the inclusion of a single shot detector (SSD) framework which presents a variety of advantages when compared to other pre-trained object detection frameworks. 22 Moreover, SSD reinforced models are the only models currently compatible with TensorFlow Lite (TFLite) conversion requirements.…”

Section: Methodology Space Imagery Databasementioning

confidence: 99%

Satellite Detection in Unresolved Space Imagery for Space Domain Awareness Using Neural Networks

Jordan¹,

Posada²,

Zuehlke³

et al. 2022

Preprint

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This work utilizes a MobileNetV2 Convolutional Neural Network (CNN) for fast, mobile detection of satellites, and rejection of stars, in cluttered unresolved space imagery. First, a custom database is created using imagery from a synthetic satellite image program and labeled with bounding boxes over satellites for "satellitepositive" images. The CNN is then trained on this database and the inference is validated by checking the accuracy of the model on an external dataset constructed of real telescope imagery. In doing so, the trained CNN provides a method of rapid satellite identification for subsequent utilization in ground-based orbit estimation.

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Section: Methodology Space Imagery Databasementioning

confidence: 99%

Satellite Detection in Unresolved Space Imagery for Space Domain Awareness Using Neural Networks

Jordan¹,

Posada²,

Zuehlke³

et al. 2022

Preprint

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show abstract

“…Layers in MobileNetV2 total 154. Compared to other popular CNN models, MobileNetV2 employs fewer parameters [38]. An effective network design with rapid execution is MobileNetV2.…”

Section: Mobilenetv2mentioning

confidence: 99%

“…Convolution filters found in deep learning architectures eliminate the requirement for manual feature extraction. Because of this, deep learning-based studies have excelled at several tasks involving the classification of medical images [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. A pre-trained CNN-based model was utilized by Lu et al [21] to identify brain cancers in MRI images.…”

Section: Introductionmentioning

confidence: 99%

Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images

Demir

Ari

Demir

2023

FUJECE

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One of the most dangerous diseases in the world is a brain tumor. A brain tumor destroys healthy tissue in the brain and then multiplies abnormally, causing increased internal pressure in the skull. This can lead to death if not diagnosed early. Magnetic Resonance Imaging (MRI) is a diagnostic method that is frequently used in soft tissues and gives successful results. In this study, a brain tumor was automatically detected from MR images. For feature extraction, a pre-trained Convolutional Neural Network (CNN) model named MobilenetV2 was used. Then, the ReliefF algorithm was used for feature selection. The features extracted with MobileNetV2 and the features selected with the ReliefF algorithm are given separately to the classifiers and the system performance is tested. As a result of experimental studies, it was seen that the highest performance was obtained with the combination of MobileNetV2 feature extraction, ReliefF algorithm feature selection, and KNN classifier.

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“…The main body of the SR-Net model uses the Inverted Residual Block, similar to MobileNet, with the input of low-dimensional features and uses Pointwise (PW) Convolution to reduce the computational complexity (Can et al, 2021).…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

A forest fire smoke detection model combining convolutional neural network and vision transformer

Gui

Tan

Yang

et al. 2023

Front. For. Glob. Change

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Forest fires seriously jeopardize forestry resources and endanger people and property. The efficient identification of forest fire smoke, generated from inadequate combustion during the early stage of forest fires, is important for the rapid detection of early forest fires. By combining the Convolutional Neural Network (CNN) and the Lightweight Vision Transformer (Lightweight ViT), this paper proposes a novel forest fire smoke detection model: the SR-Net model that recognizes forest fire smoke from inadequate combustion with satellite remote sensing images. We collect 4,000 satellite remote sensing images, 2,000 each for clouds and forest fire smoke, from Himawari-8 satellite imagery located in forest areas of China and Australia, and the image data are used for training, testing, and validation of the model at a ratio of 3:1:1. Compared with existing models, the proposed SR-Net dominates in recognition accuracy (96.9%), strongly supporting its superiority over benchmark models: MobileNet (92.0%), GoogLeNet (92.0%), ResNet50 (84.0%), and AlexNet (76.0%). Model comparison results confirm the accuracy, computational efficiency, and generality of the SR-Net model in detecting forest fire smoke with high temporal resolution remote sensing images.

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Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques

Cited by 44 publications

References 55 publications

Satellite Detection in Unresolved Space Imagery for Space Domain Awareness Using Neural Networks

Satellite Detection in Unresolved Space Imagery for Space Domain Awareness Using Neural Networks

Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images

A forest fire smoke detection model combining convolutional neural network and vision transformer

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