Identification of paddy fields based on temporal phase using Spectral Angle Mapper (SAM) on Landsat-8 OLI

Fathoni, Mohammad Naufal; Anintika, Gelanggoro K.; Firda, Dariin; Kricella, Pronika; Widyani, Prima; Arjasakusuma, Sanjiwana; Kamal, Muhammad

doi:10.1117/12.2619435

Cited by 1 publication

(2 citation statements)

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“…The appearance in the vegetative phase is in the form of young or small rice plants. The generative phase of rice growth is the phase when rice starts to flower so that its appearance in the image is dominated by green vegetation while the maturation phase is the phase when rice experiences the process of filling the grains 13 . The appearance of the image for rice in this phase will look yellow 14. Based on these phases, classification is then carried out on the image into young rice, almost harvested rice, ready to harvest rice, and dead rice.…”

Section: Image Classificationmentioning

confidence: 99%

“…The spectral library is used as a spectral reference value to identify ricefields 13 . The reflectance curve of the spectral library shows the pattern of differences in spectral reflectance of each class of rice growth phase which is then used as a reference in the classification of spectral reflectance in aerial photography images.…”

Section: Image Classificationmentioning

confidence: 99%

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Multispectral data UAV for rice growth phase: a comparison of pixel-based and object-based approach

Sasongko,

Nasrulloh,

Hadi

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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The advancement of remote sensing image acquisition through Unmanned Aerial Vehicles (UAVs) has seen rapid growth in the last five years, particularly in the field of agricultural mapping. The inclusion of multispectral sensors on UAVs holds potential and capabilities for distinguishing different growth stages of rice crops. However, with respect to this objective, there has been limited research investigating pixel-based and object-based classification approaches using multispectral UAV data. This study aims to assess the capabilities of multispectral aerial photos in identifying rice crop growth stages through both pixel-based and object-based classification methods within a portion of the Banyubiru Subdistrict, Semarang Regency. The Support Vector Machine (SVM) method is employed for pixel-based classification, while the object-based classification (OBIA) process employs the Segment Mean Shift algorithm for segmentation. Training samples and data accuracy are obtained through visual interpretation based on the developed orthomosaic data. Four rice crop growth stages are mapped, namely vegetative, reproductive, ripening, and bare-land phases. The two approaches yield differing accuracy performance. The pixel based approach using support vector machine (SVM) achieves an accuracy of 45% with a kappa coefficient of 0.28, whereas the Object Based Image Analysis (OBIA) approach attains an accuracy of 37% with a kappa coefficient of 0.24. The results indicate that, in this case, the pixel-based approach (SVM) demonstrates higher accuracy compared to the Object Based Image Analysis (OBIA) approach. However, the low accuracy indicates the limitations of pixel based image analysis using spectrometer inputs for mapping using UAV datasets.

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Section: Image Classificationmentioning

confidence: 99%

Section: Image Classificationmentioning

confidence: 99%