Roof detection on satellite images

Sağlam, Ali; Baykan, Nurdan Akhan

doi:10.1109/siu.2017.7960413

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…As an innovative contribution, the estimation based on satellite images provides another modal to the multimodal fusion to assist the prediction of the visibility. Satellite images are digital images which contain massive information, those images are taken by a satellite vehicle through some sensors which perceive the specific wavelength of the light [25]. The advanced features of satellite images allow timely delivery of high-quality image data, high agility, and high accuracy for demanding operations [26], [27].…”

Section: Satellite Imagesmentioning

confidence: 99%

Weather Visibility Prediction Based on Multimodal Fusion

Zhang

Chen

et al. 2019

IEEE Access

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Visibility affects all forms of traffic: roads, sailing, and aviation. Visibility prediction is meaningful in guiding production and life. Different from weather prediction, which relies solely on atmosphere factors, the factors that affect meteorological visibility are more complicated, such as the air pollution caused by factory exhaust emission. However, the current prediction of visibility is mostly based on the numerical prediction method similar to the weather prediction. We proposed a method using multimodal fusion to build a weather visibility prediction system in this paper. An advanced numerical prediction model and a method for emission detection were used to build a multimodal fusion visibility prediction system. We used the most advanced regression algorithm, XGBoost, and LightGBM, to train the fusion model for numerical prediction. Through the estimation of factory emission by the traditional detector in the satellite image, we propose to add the result of estimation based on Landsat-8 satellite images to assist the prediction. By testing our numerical model in atmosphere data of various meteorological observation stations in Beijing-Tianjin-Hebei region from 2002 to 2018, our numerical prediction model turns out to be more accurate than other existing methods, and after fusing with emission detection method, the accuracy of our visibility prediction system has been further improved.

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Section: Satellite Imagesmentioning

confidence: 99%

Weather Visibility Prediction Based on Multimodal Fusion

Zhang

Chen

et al. 2019

IEEE Access

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“…In image processing, the pixels belonging to two different classes can be distinguished more accurate using this method. Saglam and Baykan (2017) used LDA to define the roof pixels in the satellite images taken over from an urban (Saglam and Baykan, 2017). According to the study, the urban images are considered they consist of two classes; the roofs are handled as foreground, and the rest of the zone as background.…”

Section: Introductionmentioning

confidence: 99%

A Satellite Image Classification Approach by Using One Dimensional Discriminant Analysis

Sağlam

Baykan

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The classification problem in the image processing field is an important challenge, so that in the process image pixels are separated into previously determined classes according to their features. This process provides a meaningful knowledge about an area thanks to the satellite images. Satellite images are digital images obtained from a satellite vehicle by the way scanning the interest areas with some specified sensors. These sensors provide the specific radiometric and spatial information about the surface of the object. This information allows the researchers to obtain reliable classification results to be used to solve some real life problems such as object extraction, mapping, recognition, navigation and disaster management. Linear Discriminant Analysis (LDA) is a supervised method that reduces the dimensions of data in respect to the maximum discrimination of the elements of the data. This method also transfers the data to a new coordinate space in which the discriminant features of the classes are highest using the objection data provided manually. In this work, we consider the classes as if the satellite images have two classes; one is foreground and the other is background. The true classes such as roofs, roads, buildings, spaces and trees are treated sequentially as the foreground. The area outside the foreground class is treated as the background. The one dimensional reduced feature values of pixels, such that each value is reduced according to the binary classification of each class, are considered as membership values to the classes. In this way, each pixel has membership values for each of the classes. Finally, the pixels are classified according to the membership values. We used the ISPRS WG III/4 2D Semantic Labeling Benchmark (Vaihingen) images includes the ground truths and give the accuracy result values for each class.</p>

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Roof detection on satellite images

Cited by 2 publications

References 16 publications

Weather Visibility Prediction Based on Multimodal Fusion

Weather Visibility Prediction Based on Multimodal Fusion

A Satellite Image Classification Approach by Using One Dimensional Discriminant Analysis

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