Obtaining LULC distribution at 30-m resolution from Pixxel’s first technology demonstrator hyperspectral imagery

ABSTRACT

The recent deployment of new satellite-based hyperspectral imaging sensors, along with advances in classification methods, has enhanced the acquisition of information on the spatiotemporal patterns of Land Use/Land Cover (LULC).. With this context, the study presented in this paper seeks to assess the combined utilization of the Pixxel’s technology demonstrator hyperspectral satellite mission with the Support Vector Machines (SVM) and Random Forest (RF) machine learning classifiers for LULC mapping in an agricultural area located in Queensland, Australia. Accuracy assessment of the resulting LULC maps has been done based on relevant classification accuracy metrics. It has been observed that SVMs outperformed RF in terms of both overall classification and urban vegetation cover mapping accuracy. An overall accuracy of 92.34% with kappa of 0.90 was reported for the SVMs’ results, whereas for RF, the accuracy was 89.65% with 0.89 kappa, respectively. Both classifiers produced similar results for the classes of mango tree and sugarcane crops, whereas minor differences were observed in the depiction of water bodies and artificial surfaces. Our study findings provide insightful perspectives on the potential of Pixxel datasets for delineating the spatiotemporal distribution of LULC in agricultural landscapes, showcasing Pixxel’s hyperspectral sensors’ promising potential in this field.

KEYWORDS:

• Machine Learning
• land use/land cover mapping
• hyperspectral imagery
• Pixxel
• LULC

Acknowledgements

The authors are grateful to the anonymous reviewers for their comments that resulted in improving the initially submitted manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).