Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics

Abstract

This paper puts together some techniques that have been previously developed by the authors, but separately, relative to fuzzy classification within a remote sensing setting. Considering that each image can be represented as a graph that defines proximity between pixels, certain distances between the characteristic of contiguous pixels are defined on such a graph, so a segmentation of the image into homogeneous regions can be produced by means of a particular algorithm. Such a segmentation can be then introduced as information, previously to any classification procedure, with an expected significative improvement. In particular, we consider specific measures in order to quantify such an improvement. This approach is being illustrated with its application into a particular land surface problem.

Classifying image analysis techniques from their output

“…Another advantage of hierarchical segmentation is that it can be used to build fuzzy boundaries by a hierarchical segmentation algorithm designed by the authors 150,152,153,154,155,156 .…”

Medical Image Segmentation Based on Morphology Algorithm and FCM Algorithm

“…m represents weighted index, also known as the smoothing parameter [7]. ik  represents the membership function that is k x in any of the samples X belonging to the cluster i , distance between the pixel k and the clustering center i , Euclidean distance is used here.…”

Fuzzy image segmentation based upon hierarchical clustering

“…Based on these relations, we construct the fuzzy boundary based on an adaptation of a hierarchical segmentation algorithm designed by the authors ( [18][19][20][21][22]). This algorithm can be classified as an unsupervised, graph-based image segmentation technique (see [1,2,[23][24][25] for more details).…”