Improving the Classification Accuracy of Fragmented Cropland by using an Advanced Classification Algorithm

Fragmented crop land and marginal landholdings play an important role to classify the landuse and adopt different cropping and management practices. Here the implementation crop classification algorithms are very much difficult and produce results with lower accuracy. Static imagery captured in the optical bands are often contaminated with cloud cover and fail to detect the phenological as well as the structural changes happening during the crop growth. This is very common and most typical in Indian climatic condition. Here, during monsoon period capturing temporal satellite images of the crop periods is a very challenging task. Therefore, the present study aims at application of a novel crop classification algorithm that utilizes the temporal patterns of synthetic aperture radar (SAR) datasets from Sentinel-1 in mapping of landuse of an agriculture system, that is fragmented, small and heterogeneous in nature. Here we used different polarization of Sentinel-1 datasets and developed the temporal crop patterns of different crops grown in semi-arid region of India. Further, an advanced classification algorithm such as time weighted dynamic time wrapping (TWDTW) is employed to classify the cropland with a higher accuracy. Pixel based image analysis was carried out and tested their applicability for cropland mapping. In-situ data sets are collected from the study site to validate the exhibited results from classification outputs. The overall accuracy of the pixel based TWDTW method performed very good results with accuracy of 63 %. The Kappa coefficient is found to be 0.58. The findings confirmed that the pixel based TWDTW algorithm has the potential to delineate the croplands, which were subjected to varying irrigation treatments and management practices, using sentinel-1 datasets.

Keywords: crop classification, landuse, image analysis, Sentinel-1, TWDTW