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Determination of Spectral Signatures of Corn (Zea Mays) for Classification using Object-Based Image Analysis (OBIA) Michelle JAPITANA, Arnold APDOHAN, Anamarie PONDOG, Monalaine BERMOY, Bryan TINGAS and James Earl CUBILLAS, Philippines Key words: Corn, Spectral signature, OBIA SUMMARY This paper illustrates the utilization of the spectral data to delineate the spectral signatures of corn (Zea mays) crop for classification employing Object-Based Image Analysis (OBIA). The preliminary measurements were carried out in the selected corn areas of Butuan City, Philippines. In-situ spectral response measurement was done using Ocean OpticsTM VIS-NIR Spectrometer with spectral range from 350 nm to 1000 nm. Analysis of the spectral reflectance was conducted in order to determine the unique spectral response characteristic of corn at its varying growth stages. Findings of this study shows that discrimination of corn among other vegetation types with the use of the spectral data can enhance the ruleset development for feature extraction using OBIA. 13 th South East Asian Survey Congress Expanding the Geospatial Future 28 th – 31 st July 2015 Marina Bay Sands, Singapore 1/3

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Determination of Spectral Signatures of Corn (Zea Mays) for Classification using Object-Based Image Analysis (OBIA) Michelle JAPITANA, Arnold APDOHAN, Anamarie PONDOG, Monalaine BERMOY, Bryan TINGAS and James Earl CUBILLAS, PhilippinesKey words: Corn, Spectral signature, OBIASUMMARY

This paper illustrates the utilization of the spectral data to delineate the spectral signatures of corn (Zea mays) crop for classification employing Object-Based Image Analysis (OBIA). The preliminary measurements were carried out in the selected corn areas of Butuan City, Philippines. In-situ spectral response measurement was done using Ocean OpticsTM VIS-NIR Spectrometer with spectral range from 350 nm to 1000 nm. Analysis of the spectral reflectance was conducted in order to determine the unique spectral response characteristic of corn at its varying growth stages. Findings of this study shows that discrimination of corn among other vegetation types with the use of the spectral data can enhance the ruleset development for feature extraction using OBIA.

BIOGRAPHY

Engr. Michelle V. Japitana is the project leader of the Phil-LiDAR2 Project in Caraga State University funded by the Philippine Council for Industry, Energy, and Emerging Technology Research and Development (PCIEERD) of the Department of Science and Technology (DOST). She is an associate professor of the Geodetic Engineering Division in the College of Engineering and Information Technology of Caraga State University. She finished her degree in Bachelor of Science in Geodetic Engineering in Caraga State University and holds a degree in Master of Science in Remote Sensing from the University of the Philippines, Diliman.CONTACTSProject Leader: Michelle V. Japitana

Phil-LiDAR 2, Caraga State University

Ampayon

Butuan City

Philippines

Email: [email protected]: Anamarie Pondog

Phil-LiDAR 2, Caraga State University

Ampayon

Butuan City

Philippines

Email: [email protected] Science Research Specialist: Arnold Apdohan

Phil-LiDAR 2, Caraga State University

Ampayon

Butuan City

Philippines

Email: [email protected] Associate I: Monalaine Bermoy

Phil-LiDAR 2, Caraga State University

Ampayon

Butuan City

Philippines

Email: [email protected] Associate I: James Earl Cubillas

Phil-LiDAR 2, Caraga State University

Ampayon

Butuan City

Philippines

Email: [email protected] Associate : Bryan Tingas

Phil-LiDAR 2, Caraga State University

Ampayon

Butuan City

Philippines

Email: [email protected] 1/313th South East Asian Survey CongressExpanding the Geospatial Future

28th 31st July 2015

Marina Bay Sands, Singapore