Deep Learning for Satellite Image Classification

Shafaey, Mayar A.; Salem, Mohammed A.-M.; Ebied, Hala M.; Al-Berry, Maryam N.; Tolba, Mohamed F.

doi:10.1007/978-3-319-99010-1_35

Cited by 53 publications

(26 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, CNN models have been used widely in image classification for different applications [20,34,[40][41][42] or to extract features from the convolutional layers before or after the down sampling layers [41,43]. However, the architectures discussed above are not suitable for image segmentation or pixel-wise classifications.…”

Section: Basic Conceptsmentioning

confidence: 99%

See 1 more Smart Citation

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Almotairi

Kareem

Aouf

et al. 2020

Sensors

Self Cite

120

View full text Add to dashboard Cite

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

show abstract

Section: Basic Conceptsmentioning

confidence: 99%

“…The CNN model has different architectures-i.e., Alex Net, VGG-Net, ResNet, etc. [32][33][34]. The work presented by Bellver et al [5] used VGG-16 architecture as the base network in their work.…”

Section: Introductionmentioning

confidence: 99%

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Almotairi

Kareem

Aouf

et al. 2020

Sensors

Self Cite

120

View full text Add to dashboard Cite

show abstract

“…After the procedure is done, the car count in the scene is simply determined by the final number of found key points. Many recent works are based on deep learning for the application of counting moving vehicles or for traffic scene understanding [20,21]. Zheng et al [22], used five deep unsupervised learning models to learn driving modes.…”

Section: Vehicle Detection In Remote Sensing Images Mohammed A-m Samentioning

confidence: 99%

Vehicle Detection In Remote Sensing Images

2019

IJITEE

View full text Add to dashboard Cite

Traffic monitoring and management is one of the most crucial tasks of governing bodies in modern big cities. With each passing day the traffic problem grows in complexity due to the continuous increase of participating vehicles and the hard expansion of the road network and parking places. In this article we introduce a new method for vehicle detection and localization in parking lots using high resolution UAV images. In order to end up with practical and yet effective approach, which could be implemented on low computing hardware resources and integrated with the camera in the UAV, we considered simple steps in the proposed algorithm for optimization. It follows the machine learning pipeline such as preprocessing, sensing, feature extraction, training and classification. In preprocessing the images are thresholded iteratively in multiple color spaces to extract the candidate regions of interest (ROI). The algorithm relies on point and shape features using fast techniques in the feature extraction. The features are then clustered by the K-means algorithm and represented by the resulted clusters’ centers. Region based linear classification is finally applied using SVM to classify if the object is a vehicle or else. The proposed approach proved high detection and classification accuracy more than 86% and still running under the low complexity constraint..

show abstract

“…However, application of neural networks for object detection and contouring on satellite images is still poorly developed [37][38][39]. The traditional problem of classifying land use types and land cover is more or less solved [40][41][42][43][44][45], with less work on detecting specific objects. First of all, it is related to the availability of training data sets for the main classes of land use and land cover (AlexNet, ImageNet [46], GoogLeNet [47], VGGNet [48]).…”

Section: Introductionmentioning

confidence: 99%

Automatic Gully Detection: Neural Networks and Computer Vision

Gafurov¹,

Yermolayev²

2020

Remote Sensing

View full text Add to dashboard Cite

Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.

show abstract

Deep Learning for Satellite Image Classification

Cited by 53 publications

References 14 publications

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Vehicle Detection In Remote Sensing Images

Automatic Gully Detection: Neural Networks and Computer Vision

Contact Info

Product

Resources

About