Abstract

Spatial Data Mining or Knowledge Discovery in Spatial Databases (KDSD), i.e. mining knowledge from large amounts of spatial data, acquired at various time intervals and from different sensors, is a highly demanding field. Spatial data mining helps in unraveling interesting and previously unknown, but potentially useful patterns from spatial datasets which is more challenging because of the diversity of multi sensor’s multi resolutions (spatial, spectral and temporal) data types with spatial relationships, autocorrelation, etc.

Spatial data acquired from multi resolution remote sensing sensors (in raster format) have been used to provide insights to landscape dynamics through comprehensive understanding of land use and land cover (LULC) pattern of a region. Detailed and accurate inventorying, mapping and monitoring of LULC at a local/regional scale have been possible with the availability of various medium to high spatial resolution sensors (such as Landsat, IRS LISS-III/IV, SPOT, IKONOS, etc.). The land use (LU) information are derived using image processing techniques based on the spectral properties of objects in the bands to assign them into a user defined class label.

Successful classification of spatial data into thematic information requires efficient and optimal image processing. Appropriate classification techniques and feature selection would enhance the classification accuracy. In this context, this chapter presents various advanced supervised pattern classification algorithms such as Maximum Likelihood Classifier, Decision Tree, K-Nearest Neighbour, Neural Network, Random Forest, Contextual Classification using sequential maximum a posteriori estimation, and Support Vector Machine, apart from their implementation on multi-resolution data acquired from different remote sensing satellites along with an assessment of the classifiers’ performance. Hybrid Bayesian Classifier a novel classification technique is presented with case studies in the last section of the chapter.

Keywords: Bayesian classifier, classification, machine learning, multi-resolution data, remote sensing, spatial data mining.