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Non-linear separation of classes using a kernel based fuzzy c-means (KFCM) approach

In remote sensing applications, the classification of pixels into non-heterogeneous groups is an essential task, where each group belongs to some particular land cover type This problem is modeled as a supervised classification or a clustering problem However, it requires having exact number of the true clusters in satellite images The cluster algorithms require to be able to find the number of clusters But, in satellite images, some clusters occupy very few pixels, while some neighboring regions or groups of such, widely varying sizes, presents a challenge in designing segmentation algorithm Many supervised classification techniques exist in the literature to solve the image classification problem It is also possible to classify the remote sensing image to clearly describe certain land feature classes such as roads, streets and other features As a prerequisite, certain land feature classes with spectral information with known values is to be used in the fuzzy classification An integrated method consisting of maximum likelihood and fuzzy classification is proposed in this paper In this method, a suitable partitioning is detected in an iterated manner In this paper, first maximum likelihood (ML) supervised and then fuzzy classification is done The fuzzy rule sets are framed to identify the certain land features Certain linguistic variables are used along with membership functions The estimated area (% of pixels) of class is calculated, according to rules defined in the fuzzy classification system The resultant classes of both the procedures are compared with k-means, isodata, ML classification algorithms The accuracy of analysis is done by comparing percentage of pixels occurred in the classes The fuzzy based classification method, found to give better results This technique could be applied in several applications such as land use classification, hydrological applications, soil mapping, clustering, decision making, and operations research

Classification of high resolution pixels into non-heterogeneous groups using rule based fuzzy approach

Remote sensing data have been used for effective recognition and classification of land use and land cover features on Earth surface. This paper presents a new approach of handling mixed pixel problem present in remote sensing data and nonlinearity between class boundaries through incorporating the kernel functions with noise classifier (NC). Kernel functions have been combined with conventional noise clustering without entropy, classification method (KNC) to classify data obtained from Landsat-8 and Formosat-2 satellites. Simulated image technique has been introduced and used to handle mixed pixel problem and to assess the results of adopted classification method (KNC). The procedure includes optimization of parameters for nine different kernels, finding the best performing kernel, and image to image accuracy assessment using fuzzy error matrix. A comparative analysis of kernel-based noise classifier over conventional NC classifier has also been included in this paper.

Performance Evaluation of Kernel-Based Supervised Noise Clustering Approach

Today the field of remote sensing has become exciting and glamorous with rapidly expanding opportunities. Many organizations spend large amount of money on these fields. Two main reasons why these fields are so important in recent years are: 1) Now-a-days scientists, researchers, students, and even common people are showing great interest for better understanding of our environment i.e., the geographic space of their study area and the events that take place there. 2) With the development in sophisticated space technology (which can provide large volume of spatial data), along with declining costs of computer hardware and software has made Remote Sensing affordable to not only complex environmental/ spatial situation but also affordable to an increasingly wider audience. Now a day it is a tough task to handle large amount of remote sensing data and to obtain information relating land cover mapping. Classification of images is one of the important process in this area. Effective use of various features of remotely sensed data and the selection of suitable classification method are especially important for improving classification accuracy. This paper explains how remote sensing data with uncertainty are dealt with fuzzy based classification using kernel approach. Here we mainly study FCM with global kernels.

Analysis of Global Kernels Using Fuzzy C Means Algorithm

Remote sensing has become one of a new and interesting area in the field of computer science. Today we have to handle a large amount of remote sensing images to obtain information concerning land cover mapping. Image classification is one of the important process in this field. Effectual use of different features of remotely sensed data and the selection of suitable classification method are especially important for improving classification accuracy. This paper explains how remote sensing data with uncertainty are dealt with fuzzy based classification using kernel approach. Here we mainly study FCM with local kernels.

Study of Local Kernel with Fuzzy C Mean Algorithm

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Analysis of Global Kernels Using Fuzzy C Means Algorithm

Study of Local Kernel with Fuzzy C Mean Algorithm