Neural Network Based Pavement Condition Assessment with Hyperspectral Images

Date

2020-12-01

Author

Özdemir, Okan Bilge
Soydan, Hilal
Yardimci Cetin, Yasemin
Düzgün, Hafize Şebnem

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

236
views

0
downloads

Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. This study introduces a novel age-based pavement assessment method, employing an integrated algorithm with artificial neural network (ANN) and spectral angle mapping (SAM) on hyperspectral images. In the proposed method, the resulting ANN prediction outputs are used to make a new prediction along with the results from SAM scores. Tests are performed on hyperspectral images that have 360 spectral bands between 400 and 900 nm, collected by a specifically designed vehicular system for proximal image acquisition. The acquired images have eight classes, including three different pavement classes (good (5-year), medium (10-year), and poor (25-year)), yellow dye, white dye, soil, paving stone, and shadow. Several experiments are performed to evaluate the robustness of the followed methodology with limited learning data that include 5, 10, 25, and 50 samples per class, selected randomly from our independent spectral database. For a fair comparison, the individual ANN, SAM, support vector machine (SVM), and stacked auto-encoders (SAE) algorithms are also evaluated. The classification performances of individual ANN and SAM are significantly increased with their joint use, demonstrating a 1.2% to 21% classification accuracy improvement in relation to the training sample size. The study proves that the proposed approach is quite robust in cases wherein few training data are available, while SAE and standard ANN algorithms are more successful in cases wherein more learning data are present.

Subject Keywords

General Earth and Planetary Sciences

URI

https://hdl.handle.net/11511/88501

Journal

REMOTE SENSING

DOI

https://doi.org/10.3390/rs12233931

Collections

Graduate School of Informatics, Article

Suggestions

OpenMETU
Core

Integration of environmental variables with satellite images in regional scale vegetation classification Domaç, Ayşegül; Süzen, Mehmet Lütfi; Bilgin, Cemal Can (Informa UK Limited, 2006-04-01) The difficulty of collecting information at conventional field studies and relatively coarse spatial and spectral resolution of Landsat images forced the use of environmental variables as ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos Mountains region of southern central Turkey. In the first part of the study, ordinary vegetation classification is attained by using a maximu...
Evaluation of cross-track illumination in EO-1 Hyperion imagery for lithological mapping San, B. Taner; Süzen, Mehmet Lütfi (Informa UK Limited, 2011-01-01) Hyperspectral remote sensing data is a powerful tool for discriminating lithological units and for the preparation of mineral maps for alteration studies. The spaceborne hyperspectral Hyperion sensor, despite its narrow swath width (similar to 7.5 km), possesses great potential with its 196 channels within the wavelength range 426.82-2395.50 nm. Although it has many advantages such as low cost and on-demand coverage, much uncertainty exists in the utility of its applications. For example, poor signal-to-noi...
Digital elevation model (DEM) generation and accuracy assessment from ASTER stereo data SAN, BEKİR TANER; Süzen, Mehmet Lütfi (Informa UK Limited, 2005-11-20) Digital elevation models (DEM) are the indispensable quantitative environmental variable in most of the research studies in remote sensing. The improvement of sensor and satellite imaging technologies enabled the researchers to generate DEM using remotely sensed data. These data can be started to use as not only the two-dimensional (2-D) but also three-dimensional (3-D) information sources with usage of the DEM. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one of the sensor ...
Composite landfill liner design with Ankara clay, Turkey Met, İlker; Akgün, Haluk (Springer Science and Business Media LLC, 2005-04-01) This study presents an overview of the geotechnical properties of the clayey soils, referred to as "Ankara clay", at two sites of the Ankara region in an attempt to design a landfill profile composed of a high density polyethylene (HDPE) geomembrane/clay composite liner through the Hydrologic Evaluation of Landfill Performance (HELP) model and the Water Balance Method. The geotechnical properties of the landfill layers along with the water balance factors (i.e., evapotranspiration, precipitation, temperatur...
Local Primitive Pattern for the Classification of SAR Images AYTEKİN, orsan; KOÇ, mehmet; Ulusoy, İlkay (Institute of Electrical and Electronics Engineers (IEEE), 2013-04-01) This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining the intensity information of pixels with spatial information and structural relationships. Unlike classical approaches which define a static neighborhood via a rectangular moving window of predefined size and relate spatial information for each center pixel to all the pixels within that window, the local primitives (LPs) proposed in this study provide...

Citation Formats

O. B. Özdemir, H. Soydan, Y. Yardimci Cetin, and H. Ş. Düzgün, “Neural Network Based Pavement Condition Assessment with Hyperspectral Images,” REMOTE SENSING, pp. 0–0, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/88501.