Unsupervised Classification

Introduction:

Classification of Land Use / Land Cover (LULC) information is one of the primary applications of remote sensing. There are several different methods for performing LULC classification, and in this lab I will be exploring "Unsupervised Classification" using the ISODATA method.

Methods:

ISODATA (Iterative self-organizing data analysis algorithm) identifies common spectral values, and organizes them into clusters. It doesn't require any in-situ data, or any knowledge of the study area before it organizes the pixels. Parameters that I changed are: the maximum number of model iterations, the convergence threshold between spectral values, and the minimum and maximum number of classes into which the data will be organized.

After the pixels were organized into clusters, I recoded them into 5 land use classes: water, forest, agriculture, urban/built-up, and bare soil. I ran the ISODATA algorithm twice, so it sorted the information into 10 classes the first time, and 20 classes the second time.

Results:

The resulting classified images suited the area fairly well (Figure1). The 20-class iteration produced a better result, as the additional classes allowed it to capture a greater variety extent of spectral variance within each LULC class.

Figure 1: The results of the 20-class ISODATA algorithm.

Conclusion:

The ISODATA algorithm is fairly simple, but manages to produce good results in spite of its simplicity. The major benefit of ISODATA is how it doesn't require in-depth knowledge of the study area before implementation, unlike other methods. If better results were desired, I could increase the number of iterations and increase the number of spectral classes.