Fuzzy Classification of Remote Sensing Image.

Remote Sensing Image Classification

Remote Sensing Image Classification: A Neuro-fuzzy MCS Approach

The present article proposes a new neuro-fuzzy-fusion (NFF) method for combining the output of a set of fuzzy classifiers in a multiple classifier system (MCS) framework. In the proposed method the output of a set of classifiers (i.e., fuzzy class labels) are fed as input to a neural network, which performs the fusion task. The proposed fusion technique is tested on a set of remote sensing imag...

Remote Sensing Image Classification Using Fuzzy- PSO Hybrid Approach

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify cluste...

Fuzzy Supervised Classification of Remote Sensing Images

Abstruct-In the conventional remote sensing supervised classification, training information and classification results are represented in a one-pixel-one-class method. Class mixture cannot be taken into consideration in training a classifier and in determining pixels’ membership. The expressive limitation has reduced the classification accuracy level and led to the poor extraction of informatio...

Fuzzy sets in remote sensing classification

Supervised classification in remote sensing is a very complex problem and involves steps of different nature, including a serious data preprocessing. The final objective can be stated in terms of a classification of isolated pixels between classes, which can be either previously known or not (for example, different land uses), but with no particular shape nither well defined borders. Hence, a f...