Regenerating forest is important to account for carbon sink. Mapping regenerating forest from satellite data is difficult because it is spectrally mixed with natural forest. This paper investigated the combined use of texture features and voting classifications to enhance recognition of these two classes. Bagging and boosting were applied on Learning Vector Quantization (LVQ) and decision tree. Our results show that spatial textures improved separability. After applying voting classifications, class accuracy of decision tree increased by 5–7% and that of LVQ by approximately 3%. Substantial reduction (between 23% to 40%) of confusions between regenerating forest and natural forest were recorded. Comparatively, bagging is more consistent than boosting. An interesting observation is that even LVQ, a stable learner, was able to benefit from both voting classification algorithms.