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Unsupervised Pixel Classification in Satellite Imagery: A Two-stage Fuzzy Clustering Approach

Mukhopadhyay A., Maulik U.

Fundamenta Informaticae

2008

Vol. 86, nr 4

411-428

A popular approach for landcover classification in remotely sensed satellite images is clustering the pixels in the spectral domain into several fuzzy partitions. It has been observed that performance of the clustering algorithms deteriorate with more and more overlaps in the data sets. Motivated by this observation, in this article a two-stage fuzzy clustering algorithm is described that utilizes the concept of points having significant membership to multiple classes. The points situated in the overlapped regions of different clusters are first identified and excluded from consideration while clustering. Thereafter, these points are given class labels based on Support vector Machine classifier which is trained by the remaining points. The well known Fuzzy C-Means algorithm and some recently proposed genetic clustering schemes are utilized in the process. The effectiveness of the two-stage clustering technique has been demonstrated on IRS remote sensing satellite images of the cities of Bombay and Calcutta and compared with other well known clustering techniques. Also statistical significance test has been carried out to establish the statistical significance of the clustering results.

Fuzzy Symmetry Based Real-Coded Genetic Clustering Technique for Automatic Pixel Classification in Remote Sensing Imagery

Saha S., Bandyopadhyay S.

Fundamenta Informaticae

2008

Vol. 84, nr 3-4

471-492

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes presents a challenging task. In this paper, a newly developed real-coded variable string length genetic fuzzy clustering technique with a new point symmetry distance is used for this purpose. The proposed algorithm is capable of automatically determining the number of segments present in an image. Here assignment of pixels to different clusters is done based on the point symmetry based distance rather than the Euclidean distance. The cluster centers are encoded in the chromosomes, and a newly developed fuzzy point symmetry distance based cluster validity index, FSym-index, is used as a measure of the validity of the corresponding partition. This validity index is able to correctly indicate presence of clusters of different sizes and shapes as long as they are internally symmetrical. The space and time complexities of the proposed algorithm are also derived. The effectiveness of the proposed technique is first demonstrated in identifying two small objects from a large background from an artificially generated image and then in identifying different landcover regions in remote sensing imagery. Results are compared with those obtained using the well known fuzzy C-means algorithm both qualitatively and quantitatively.

Relation between VGA-classifier and MLP : determination of network architecture

Bandyopadhyay S., Pal S.K.

Fundamenta Informaticae

1999

Vol. 37, Nr 1,2

177-199

An analogy between a genetic algorithm based pattern classification scheme (where hyper-planes are used to approximate the class boundaries through searching) and multiplayer per-ceptron (MLP) based classifier is established. Based on this, a method for determining the MLP architecture automatically is described. It is shown that the architecture would need at-most two hidden layers, the neurons of which are responsible for generating hyperplanes and regions. The neurons in the second hidden and output layers perform the AND & OR func-tions respectively. The methodology also includes a post processing step which automatically removes any redundant neuron in the hidden/output layer. An extensive comparative study of the performance of the MLP, thus derived using the proposed method, with those of several other conventional MLP's is presented for different data sets.