Applications of unsupervised auto segmentation on Dhule area hyperspectral image for drought and yield prediction

Farmers in India have small land holdings and due to this, analyzing hyperspectral images becomes an issue. Due to a high probability of obstacles in small land holding areas, hyper spectral images will give less accuracy. So, the major concern will be to remove obstacles in the small land holdings by using an unsupervised segmentation method. The base data set used consists of hyperspectral images procured from the earth-explorer website. In the experiment, an image was first segmented, and its individual segments are plotted as vertices on a segmentation graph; which coupled with a corresponding vertex gives a walk-based graph kernel. The next step in the process is the Support Vector Machine (SVM), which computes the Normalized Deviation Vegetation Index (NDVI); which is then used to compute the Standard Precipitation Indices (SPI). Now the SPI threshold is applied to understand the drought severity in the area and NDVI helps in analyzing the crop yield.