Classification and Change Detection of Tirupati Urban Area using Erosion and Dilation Based PCA Transform

Abstract

In remote sensing, the identification of the land use and land cover (LULC) changes in the global and local region are developed by classification and detection algorithms. This classification system can be developed to meet the needs of state agencies, and Federal for an up-to-date analyze of LULC throughout the entire selected of region area. The multispectral images have multiple low-resolution bands due to lack of sensory acquisition problem, haze-covered on earth objects and atmospheric distributions. So difficult to analyze the full information, the user wrongly interprets the information. Image processing applications can be done for compress and enhance the details of land surface details. The Principal Component Analysis and Morphological operations are implemented for compressing and feature extract the color and earth object values with good accuracy level. Change Detection between the time difference of the proposed enhanced images for land objects classes was computed. The most extensive land cover change category identification of the Tirupati urban Agricultural and forest area for the last 14 years. The change analyzed by using the image differencemethod for obtaining the changing level of the forest and urban development areas between two-timeintervals.