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IJSTR >> Volume 9 - Issue 9, September 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Calibration And Testing A Low-Cost Spectrometer For Ground Measurement Mustafa

[Full Text]



Mustafa Mathanna Najam Shahrabani, Shattri Bin Mansor



Low-cost spectrometer, image classification, spectral library, spectrometer calibration, hand held spectrometer, ground measurements, Hyperspectral sensors.



Over recent years, a hand held spectrometer has emerged, which is divided into three classes as per their cost and uses: smartphone spectrometer; miniature spectroscopic; and low-cost spectrometer products marketed directly to consumers. The integration between spectroscopic devices and satellite imagery helps to enhance and increase the accuracy in post-processing steps of remote sensing imagery. This paper tries to test the capability of the device using an inexpensive spectrometer as ground measurements to improve the accuracy of imaging classification. Thereby, a low-cost spectrometer device has proposed in this research to test and evaluate the performance by comparing the spectral signature with USGS spectral library. The main goal of this research is to calibrate and test (accuracy and performance) of handheld spectrometer AS7265X multi-spectral sensor device for urban area application in order to enhance the remote sensing imagery classification and evaluate the accuracy of the AS7265X spectrometer device by comparing it with spectral library. Hence, the results show good discrimination proficiency for AS7265X handheld spectrometer for leaf vegetation in the field. The above results verified the reliability of the AS7265X through field measurement with USGU spectral library extracted for ENVI software in order to compare the spectral signature and obtain the accuracy.



[1] Samsudin, S. H., Shafri, H. Z., Hamedianfar, A., & Mansor, S. (2015). Spectral feature selection and classification of roofing materials using field spectroscopy data. Journal of Applied Remote Sensing, 9(1), 095079.
[2] Tzelidi, D., Stagakis, S., Mitraka, Z., & Chrysoulakis, N. (2019). Detailed urban surface characterization using spectra from enhanced spatial resolution Sentinel-2 imagery and a hierarchical multiple endmember spectral mixture analysis approach. Journal of Applied Remote Sensing, 13(1), 016514.
[3] Bello, O. M., & Aina, Y. A. (2014). Satellite remote sensing as a tool in disaster management and sustainable development: towards a synergistic approach. Procedia-Social and Behavioral Sciences, 120, 365-373.
[4] Chen, D., Liu, Z., Wang, L., Dou, M., Chen, J., & Li, H. (2013). Natural disaster monitoring with wireless sensor networks: a case study of data-intensive applications upon low-cost scalable systems. Mobile Networks and Applications, 18(5), 651-663.
[5] Jiang, L., Deng, X., & Seto, K. C. (2013). The impact of urban expansion on agricultural land use intensity in China. Land Use Policy, 35, 33-39. Jilge, M., Heiden, U., Habermeyer, M., Mende, A., & Juergens, C. (2017). Detecting unknown artificial urban surface materials based on spectral dissimilarity analysis. Sensors, 17(8), 1826.
[6] Calugaru, A., Anca, P. F., & Vasile, A. (2016). 3D cartography in urban environments for municipal administrations. Paper presented at the 6th INTERNATIONAL CONFERENCE ON CARTOGRAPHY AND GIS.
[7] Padmanaban, R., Bhowmik, A. K., Cabral, P., Zamyatin, A., Almegdadi, O., & Wang, S. (2017). Modelling urban sprawl using remotely sensed data: A case study of Chennai city, Tamilnadu. Entropy, 19(4), 163.
[8] Liu, X., He, J., Yao, Y., Zhang, J., Liang, H., Wang, H., & Hong, Y. (2017). Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science, 31(8), 1675-1696.
[9] Xu, Y., Wu, L., Xie, Z., & Chen, Z. (2018). Building extraction in very high-resolution remote sensing imagery using deep learning and guided filters. Remote Sensing, 10(1), 144.
[10] Burkart, A., Cogliati, S., Schickling, A., & Rascher, U. (2013). A novel UAV-based ultra-light weight spectrometer for field spectroscopy. IEEE sensors journal, 14(1), 62-67.
[11] Samsudin, S. H., Shafri, H. Z., & Hamedianfar, A. (2016). Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data. Journal of Applied Remote Sensing, 10(2), 025021.
[12] Jilge, M., Heiden, U., Habermeyer, M., Mende, A., & Juergens, C. (2017). Detecting unknown artificial urban surface materials based on spectral dissimilarity analysis. Sensors, 17(8), 1826.
[13] Gatebe, C. K., & King, M. D. (2016). Airborne spectral BRDF of various surface types (ocean, vegetation, snow, desert, wetlands, cloud decks, smoke layers) for remote sensing applications. Remote Sensing of Environment, 179, 131-148.
[14] Jensen, J. R. (2009). Remote sensing of the environment: An earth resource perspective 2/e: Pearson Education India.
[15] https://ams.com/as7265x#tab/documents
[16] Chappell, A., Webb, N. P., Guerschman, J. P., Thomas, D. T., Mata, G., Handcock, R. N., . . . Butler, H. J. (2018). Improving ground cover monitoring for wind erosion assessment using MODIS BRDF parameters. Remote Sensing of Environment, 204, 756-768.
[17] Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., . . . Muller, J.-P. (2002). First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1-2), 135148.
[18] Hobley, E., & Prater, I. (2019). Estimating soil texture from vis–NIR spectra. European Journal of Soil Science, 70(1), 83-95. Huang, B., Zhao, B., & Song, Y. (2018). Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214, 73-86.
[19] Crocombe, R. A. (2018). Portable Spectroscopy. Applied spectroscopy, 72(12), 1701-1751.
[20] Scheeline, A. (2017). How to design a spectrometer. Applied spectroscopy, 71(10), 2237-2252.
[21] Singh, S. K., Srivastava, P. K., Szabó, S., Petropoulos, G. P., Gupta, M., & Islam, T. (2017). Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets. Geocarto International, 32(2), 113-127.
[22] Elvanidi, A., Katsoulas, N., Bartzanas, T., Ferentinos, K., & Kittas, C. (2017). Crop water status assessment in controlled environment using crop reflectance and temperature measurements. Precision agriculture, 18(3), 332-349.
[23] Clark, A. D. (1993). U.S. Patent No. 5,253,325. Washington, DC: U.S. Patent and Trademark Office.
[24] Seager, S., Turner, E. L., Schafer, J., & Ford, E. B. (2005). Vegetation's red edge: a possible spectroscopic biosignature of extraterrestrial plants. Astrobiology, 5(3), 372-390.