Extending the Pairwise Separability Index for Multicrop Identification Using Time-Series MODIS Images

The pairwise separability index (SI) has been demonstrated as an effective indicator for capturing crucial phenological differences between two plant species. However, its application to crop types, which have more obvious phenological characteristics than natural vegetation, has received less attention, and extending the pairwise SI to multiple crops for feature selection still remains a challenge. This paper presented two SI extension approaches (SI
_ave
and SI
_min
) to select the optimal spectro-temporal features for multiple crops, and investigated their classification performance using Heilongjiang Province, China, as a study area. Feature interpretability and classification accuracy of different crops were evaluated for the two approaches. The results showed that the SI
_ave
approach generally has relatively high feature interpretability due to its better description of crucial phenological characteristics of different crops. Those crops with high separability are insensitive to the extension approach and have similar classification accuracy for the two approaches, whereas those crops with poor separability show good performance with the SI
_min
method. Due to the higher temporal autocorrelation, the optimal features for crop classification that are selected by the SI
_ave
approach exhibit greater information redundancy across the time domain than those that are selected by the SI
_min
approach, which largely explains the relatively low classification accuracy achieved using the SI
_ave
approach. These comparison results between SI
_min
and SI
_ave
approaches also indicate that time-series images with high temporal resolution do not necessarily produce high classification accuracy, regardless of their ability to describe the seasonal characteristics of crops.