Road Detection from High Satellite Images Using Neural Networks

Kahraman, I.; Turan, Muhammed Kamil; Karaş, İsmail Rakıp

doi:10.7763/ijmo.2015.v5.479

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…Therefore, the constant updating of road databases is necessary to achieve several geospatial information systems (GIS) goals, such as emergency functions, automated means of navigation, urban planning, and traffic control [4]. A road database can be created and updated using feature extraction from spatial high-resolution satellite imagery [5]. Consequently, generating automatic novel techniques for extracting road classes from high-resolution satellite images and keeping road networks up-to-date in GIS databases are useful for a variety of applications [6].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

et al. 2020

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One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

et al. 2020

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“…Two methods are used for unsupervised image classifications by using application [11], [21]. The application or software used can perform unknown pixel separation based on the bounce value in the classifications rather than by direction.…”

Section: Image Classification Techniquesmentioning

confidence: 99%

“…Some studies researched high-resolution remote sensing imagery with the supervised segmentation method; the method enhances the expression of uncertainty for pixel membership [6], [9], [10]. Other studies observe the storm, using satellite-based techniques such as Tropical Analysis and Forecast Branch, Analysis of satellite imagery with Dvorak Advanced techniques, or abbreviated to ADT used by the institute or university of Wisconsin-Madison [2], [11]. Satellite imagery data from the National Oceanic and Atmospheric Administration and the National Aeronautics and Space Administration are commonly used for weather forecasts, including tropical cyclones, storm, and rainfall [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

“…The Supervised Classification method was used with the Supervised Image Classification model through the image segmentation process using an Edge detection algorithm and Next to get the prediction value by using Spectral Angle Mapper (SAM) algorithm [11], [15], [16]. The model is proposed because it is relatively simple, which provides predictive value and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Image Classification of Chaos Phenomenon in Cumulonimbus Cloud Using Spectral Angle Mapper

Wanayumini

Sitompul

Suwilo

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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In the field of remote sensing, in addition to the weather forecast, atmospheric dynamics, oceans, cloud cumulonimbus, and Tornado are part of the phenomenon of chaos. Because in the clouds cumulonimbus, there are some layers with a gray border indicating irregular and uncertain. There is a boundary line on the layers of Cumulonimbus Clouds that could be identified based on the pixel where the differences in the intensity values are extremes. A cloud layer cumulonimbus with a gray edge border can be used as the basis for predicting the occurrence of a tornado based on a pixel location that has specific characteristics. In this research, a Supervised Image Classification algorithm with Spectral Angle Mapper was performed to get the minimum and maximum pixel intensity interval values based on spectral angles in cumulonimbus clouds. Spectral angles allow for quick mapping in determining the spectral similarities between two spectrums on cumulonimbus cloud layers. The spectral similarities are calculated by referring to the angle between the spectral forming the same dimensional vector space on the RGB color spectrum. Early detection in cumulonimbus cloud layers will indicate the occurrence of chaos phenomenon, which could be used to predict tornadoes. The results showed that the Spectral Angle Mapper approach gave minimum and maximum pixel intensity values interval of the Average Correlation Angle in the dataset image Cumulonimbus Cloud with a classification accuracy value of 95.83%.

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“…A road detection strategy using the ANN method for the classifier was introduced by M. Mokhtarzade, H. Ebadi, and M. J. Valadan Zoej, where the dataset was satellite imagery [5]. Researches done by I. Kahraman, M. Kamil Turan, and I. Rakip Karas also apply ANN in detecting road cracks with results that the ANN method is able to detect road cracks with 93.35% success [18]. Referring to those researches, this study proposes ANN method in detecting road crack and also applies GLCM to extract features from images into quantitative data.…”

Section: Introductionmentioning

confidence: 99%

Application of neural network method for road crack detection

2020

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The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in implementing Supervised Learning in computer vision, the applied method for classification is artificial neural network (ANN). Datasets, which are used for evaluation are collected from low-cost smart phones. The results show that feature extraction using GLCM can provide good accuracy that is equal to 90%.

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Road Detection from High Satellite Images Using Neural Networks

Cited by 10 publications

References 5 publications

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

Supervised Image Classification of Chaos Phenomenon in Cumulonimbus Cloud Using Spectral Angle Mapper

Application of neural network method for road crack detection

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