Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images

• Published in: 2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) pp. 104 - 108
• Main Authors: Heryadi, Yaya, Irwansyah, Edy, Miranda, Eka, Soeparno, Haryono, Herlawati, Hashimoto, Kiyota
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21-12-2022
• Subjects: Crops; Deep learning; paddy field mapping; Predictive models; Productivity; productivity prediction; satellite imagery; Satellites; Semantic segmentation; Training
• ISSN: 2771-6600
• DOI: 10.1109/AGERS56232.2022.10093406

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops.