Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

Aguilar, Manuel A.; Fernández, Antonio; Aguilar, Fernando J.; Bianconi, Francesco; Lorca, Andrés García

doi:10.5721/eujrs20164906

Cited by 16 publications

(11 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The other parameters for the SVM were automatically selected during the training stage looking for C among (1,10,100,1000) and for γ among (0.01,0.001,0.0001). For the MLP classifier, the number of hidden layers for each feature vector was automatically chosen in between (1,2,3). The number of neurons for the first hidden layers was The best split quality criterion was chosen between the Gini impurity and the information gain as described in [46].…”

Section: Resultsmentioning

confidence: 99%

Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

Ratajczak

Crispim-Junior

Faure

et al. 2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

The land cover reconstruction from monochromatic historical aerial images is a challenging task that has recently known an increasing interest from the scientific community with the proliferation of large scale epidemiological studies involving retrospective analysis of spatial pattern. However, the efforts engaged by the computer vision community in remote sensing applications are mostly focused on prospective approaches through the analysis of high resolution multi-spectral data acquired by advanced spatial programs. Hence, four contributions are proposed in this article. They aim at providing a comparison basis for the future development of computer vision algorithms applied to the automation of the land cover reconstruction from monochromatic historical aerial images. Firstly, a new multiscale multi-date dataset composed of 4.9 million non-overlapping annotated patches of the France territory between 1970 and 1990 has been created with the help of Geography experts. This dataset has been named HistAerial. Secondly, an extensive comparison study of state-of-the-art texture features extraction and classification algorithms including deep convolutional neural networks (DCNNs) has been performed. It is presented in the form of an evaluation. Thirdly, a novel low-dimensional local texture filter named Rotated-CorneR Local Binary Pattern (R-CRLBP) is presented as a simplification of the Binary Gradient Contours filter through the use of an orthogonal combination representation. Finally, a novel combination of low-dimensional texture descriptors, including the R-CRLBP filter, is introduced as a Light Combination of Local Binary Patterns (LCoLBP). The LCoLBP filter achieved state-of-the-art results on the HistAerial dataset while conserving a relatively low-dimensional feature vector space compared with the DCNN approaches (17 times shorter).

show abstract

Section: Resultsmentioning

confidence: 99%

Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

Ratajczak

Crispim-Junior

Faure

et al. 2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Jain et al (1995) defined texture as the repeating patterns of the local variations in the intensity of Digital Number (DN) values, which are too fine to be distinguished as separate objects. On high-resolution images, texture analysis has been shown to provide supplementary information on the image properties and improve classification accuracy (Aguilar et al 2016). In this work, two widely available texture measures, Gabor filter and Laplacian of Gaussian operator, were used.…”

Section: Texture Analysismentioning

confidence: 99%

Electricity consumption patterns within cities: application of a data-driven settlement characterization method

Chowdhury

Weaver

Weber

et al. 2019

International Journal of Digital Earth

View full text Add to dashboard Cite

Urban areas presently consume around 75% of global primary energy supply, which is expected to significantly increase in the future due to urban growth. Having sustainable, universal energy access is a pressing challenge for most parts of the globe. Understanding urban energy consumption patterns may help to address the challenges to urban sustainability and energy security. However, urban energy analyses are severely limited by the lack of urban energy data. Such datasets are virtually non-existent for the developing countries. As per current projections, most of the new urban growth is bound to occur in these data-starved regions. Hence, there is an urgent need of research methods for monitoring and quantifying urban energy utilization patterns. Here, we apply a data-driven approach to characterize urban settlements based on their formality, which is then used to assess intraurban urban energy consumption in Johannesburg, South Africa; Sana'a, Yemen; and Ndola, Zambia. Electricity is the fastest growing energy fuel. By analyzing the relationship between the settlement types and the corresponding nighttime light emission, a proxy of electricity consumption, we assess the differential electricity consumption patterns. Our study presents a simple and scalable solution to fill the present data void to understand intra-city electricity consumption patterns.

show abstract

“…-data not available), and significant progress in the field of modern information processing technologies for satellite imagery, on the other hand, have significantly accelerated the process of collecting and processing spatial data (Derkzen, 2015, Makarov, 2011, Mozgoviy, 2007. At present, numerous methods have been developed to delineate vegetation and water bodies in moderate-resolution satellite imagery (Aguilar, 2016, Feyisa, 2014, Xie, 2016, Hnatushenko, 2016a, Liu, 2013, Tulbure, 2013. But their use in recognizing images of high spatial resolution is ineffective.…”

Section: Introductionmentioning

confidence: 99%

Automated Recognition of Vegetation and Water Bodies on the Territory of Megacities in Satellite Images of Visible and Ir Bands

Mozgovoy

Hnatushenko

Vasyliev

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Vegetation and water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. A methodology of automated recognition of vegetation and water bodies on the territory of megacities in satellite images of sub-meter spatial resolution of the visible and IR bands is proposed. By processing multispectral images from the satellite SuperView-1A, vector layers of recognized plant and water objects were obtained. Analysis of the results of image processing showed a sufficiently high accuracy of the delineation of the boundaries of recognized objects and a good separation of classes. The developed methodology provides a significant increase of the efficiency and reliability of updating maps of large cities while reducing financial costs. Due to the high degree of automation, the proposed methodology can be implemented in the form of a geo-information web service functioning in the interests of a wide range of public services and commercial institutions.

show abstract

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

Cited by 16 publications

References 51 publications

Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

Electricity consumption patterns within cities: application of a data-driven settlement characterization method

Automated Recognition of Vegetation and Water Bodies on the Territory of Megacities in Satellite Images of Visible and Ir Bands

Contact Info

Product

Resources

About