Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods |
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| Institution: | 1. Impacts of Agriculture, Forests and Ecosystem Services Division, Euro-Mediterranean Center on Climate Change (IAFES-CMCC), via Pacinotti 5, Viterbo 01100, Italy;2. Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo 01100, Italy;3. Consiglio per la ricerca in agricoltura e l''analisi dell''economia agraria, Forestry Research Centre (CRA-SEL), Via Santa Margherita 80, I-52100 Arezzo, Italy;4. Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK;5. Santa Catarina State University (UDESC), Av. Luiz de Camoes, 2090, Lages, Santa Catarina 88520-000, Brazil;6. Department of Geography, University of Hawai''i at Mānoa, 422 Saunders Hall, 2424 Maile Way, Honolulu, HI 96822, USA |
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| Abstract: | Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier. |
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| Keywords: | HJ1B ALOS/PALSAR Image fusion Land cover classification |
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