Akademik Veri Yönetim Sistemi
International Journal of Remote Sensing, cilt.47, sa.8, ss.3310-3327, 2026 (SCI-Expanded, Scopus)
The identification of land cover types using multispectral remote sensing data remains one of the most practical and widely adopted approaches. In this study, we evaluated the potential of Pixxel Tech Demo-1 (TD-1) hyperspectral imagery in comparison with Landsat-9 multispectral data for land cover classification. Specifically, this study aimed to investigate which land cover classes benefit most from the enhanced spectral resolution of hyperspectral data, leading to improved classification accuracy. To this end, CORINE 2018 data were used as a reference, focusing on seven land cover types: water, forest, rangeland, agriculture, artificial surfaces, bareland, and flooded vegetation. Both Landsat-9 and Pixxel TD-1 images were acquired at the surface reflectance level, eliminating the need for additional atmospheric correction. For classification, we employed Extreme Gradient Boosting (XGBoost), an ensemble learning algorithm known for its high accuracy and robust performance in handling complex remote sensing data. To address the high dimensionality of Pixxel TD-1 hyperspectral imagery, Principal Component Analysis (PCA) was applied, reducing the feature set to 30 components. The results consistently demonstrated the superior performance of hyperspectral data: Pixxel TD-1 achieved a significantly higher overall accuracy (91.8%) compared to Landsat-9 (87.1%). Notably, hyperspectral imagery proved especially effective in distinguishing complex land cover types such as flooded vegetation, bareland, and rangeland, as indicated by their higher F1 scores. These findings underscore the strong potential of Pixxel’s TD-1 hyperspectral sensors for advanced land cover classification.