LULC-ANALYSIS OF LAND-USE WITH THE HELP OF UNSUPERVISED CLASSIFICATION
Abstract
Land-use and vegetation cover are the natural state of the earth's surface. Remote sensing is a very important land use study (LULC) method. Various classification methods are used to analyze land cover in remote sensing. These methods do not require prior information on land cover or land use types. Two classification methods are most commonly used to analyze remote sensing images. These include controlled classification and uncontrolled classification. The objectives of the proposed work are to use unsupervised classification methods to find clusters, determine land use types, and compare these methods with interactive analysis of self-organization data (ISODATA). Hyperion sensor images were used for land use analysis. The Hyperion sensor has two hundred and forty-two bands, but fewbands provide useful information for spectral analysis. Therefore, bands that do not contain useful information are identified and removed. After processing the input image according to this algorithm, out of 242 bands, only one hundred and sixty-five bands remain. This takes into account radiometric calibration and an important correction of atmospheric factors. Then, based on the results of processing using the proposed methods, clusters are formed to study land use using a hyperspectral image. To form clusters, the pixels were grouped based on the selected data. Pixels from the same cluster have more similarity, while pixels from different clusters differ from each other. Based on the results, it is concluded that the clustering method (k-means) allows better identification or prediction of land use based on a high-resolution hyperspectral image than the Interactive Self-Organization Data Analysis (ISODATA) method. The output image, which is the result of clustering, can be used to identify different types of land use objects. The LULC classes predicted are Water Body, Agriculture Land, other Vegetation, Built Up or settlement, Bare Land and Rocky region.
