Extensions and Modifications of the Kohonen-SOM and Applications in Remote Sensing Image Analysis

Utilization of remote sensing multi- and hyperspectral imagery has shown a rapid increase in many areas of economic and scientific significance over the past ten years. Hyperspectral sensors, in particular, are capable of capturing the detailed spectral signatures that uniquely characterize a great number of diverse surface materials. Interpretation of these very high-dimensional signatures, however, has proved an insurmountable challenge for many traditional classi¿cation, clustering and visualization methods. This chapter presents spectral image analyses with Self-Organizing Maps (SOMs). Several recent extensions to the original Kohonen SOM are discussed, emphasizing the necessity of faithful topological mapping for correct interpretation. The effectiveness of the presented approaches is demonstrated through case studies on real-life multi- and hyperspectral images

The Delineation of Agricultural Management Zones with High Resolution Remotely Sensed Data

Remote sensing (RS) techniques have been widely considered to be a promising source of information for land management decisions. The objective of this study was to develop and compare different methods of delineating management zones (MZs) in a field of winter wheat. Soil and yield samples were collected, and five main crop nutrients were analyzed: total nitrogen (TN), nitrate nitrogen (NN), available phosphorus (AP), extractable potassium (EP) and organic matter (OM). At the wheat heading stage, a scene of Quickbird imagery was acquired and processed, and the optimized soil-adjusted vegetation index (OSAVI) was determined. A fuzzy k-means clustering algorithm was used to define MZs, along with fuzzy performance index (FPI), and modified partition entropy (MPE) for determining the optimal number of clusters. The results showed that the optimal number of MZs for the present study area was three. The MZs were delineated in three ways; based on soil and yield data, crop RS information and the combination of soil, yield and RS information. The evaluation of each set of MZs showed that the three methods of delineating zones can all decrease the variance of the crop nutrients, wheat spectral parameters and yield within the different zones. Considering the consistent relationship between the crop nutrients, wheat yield and the wheat spectral parameters, satellite remote sensing shows promise as a tool for assessing the variation in soil properties and yield in arable fields. The results of this study suggest that management zone delineation using RS data was reliable and feasible