Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques

Machine learning can be employed to accurately differentiate between crops (95%).Pan-sharpening Landsat 8 imagery dramatically improves crop classification accuracy (15%).Pan-sharpening Landsat 8 imagery effects classification accuracy more than image analysis method does. This study evaluates the potential of pan-sharpening multi-temporal Landsat 8 imagery for the differentiation of crops in a Mediterranean climate. Five Landsat 8 images covering the phenological stages of seven major crops types in the study area (Cape Winelands, South Africa) were acquired. A statistical pan-sharpening algorithm was used to increase the spatial resolution of the 30m multispectral bands to 15m. The pan-sharpened images and original multispectral bands were used to generate two sets of input features at 30 and 15m resolutions respectively. The two sets of spatial variables were separately used as input to decision trees (DTs), k-nearest neighbour (k-NN), support vector machine (SVM), and random forests (RF) machine learning classifiers. The analyses were carried out in both the object-based image analysis (OBIA) and pixel-based image analysis (PBIA) paradigms. For the OBIA experiments, three image segmentation scenarios were tested (good, over and under segmentation). The PBIA experiments were carried out at 30m and 15m resolutions. The results show that pan-sharpening led to dramatic (15%) improvements in classification accuracies in both the PBIA and OBIA approaches. Compared to the other classifiers, SVM consistently produced superior results. When applied to the pan-sharpened imagery SVM produced an overall accuracy of nearly 96% using OBIA, while PBIAs overall accuracy was 1.63% lower. We conclude that pan-sharpening Landsat 8 imagery is highly beneficial for classifying agricultural fields whether an object- or pixel-based approach is used.