A method of remote sensing image auto classification based on interval type-2 fuzzy c-means

2014 ◽  
Author(s):  
Xianchuan Yu ◽  
Wei Zhou ◽  
Hui He
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Fuzzy C Means ◽  
Interval Type

Remote sensing image classification based on semi-supervised adaptive interval type-2 fuzzy c-means algorithm

2019 ◽  
Vol 131 ◽  
pp. 132-143 ◽  
Author(s):  
Jindong Xu ◽  
Guozheng Feng ◽  
Tianyu Zhao ◽  
Xiao Sun ◽  
Meng Zhu
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Fuzzy C Means ◽  
Remote Sensing Image Classification ◽  
Interval Type ◽  
Fuzzy C Means Algorithm

scholarly journals INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 385-391 ◽  
Author(s):  
Chunyan Wang ◽  
Aigong Xu ◽  
Chao Li ◽  
Xuemei Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Fuzzy Model ◽  
Remote Sensing Image ◽  
Fuzzy Membership ◽  
Fuzzy Membership Functions ◽  
Interval Type

Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

scholarly journals Modelling the Spectral Uncertainty of Geographic Features in High-Resolution Remote Sensing Images: Semi-Supervising and Weighted Interval Type-2 Fuzzy C-Means Clustering

2019 ◽  
Vol 11 (15) ◽  
pp. 1750
Author(s):  
Jifa Guo ◽  
Shihong Du ◽  
Hongyuan Huo ◽  
Shouji Du ◽  
Xiuyuan Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Confusion Matrix ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Fuzzy C Means ◽  
Spectral Curves ◽  
Validity Indices ◽  
Interval Type

The spectral uncertainty refers to the diversity and variations of spectral characteristics within a single geographic object or across different objects of the same class. Usually, existing methods represent the spectral characteristics as precise single-valued curves. Thus, the spectral variations cannot be modeled, which further restricts the analysis and classification performance of remote sensing images. On the other hand, unsupervised methods have poor performance in classification and modeling uncertainty, while supervised methods need a large number of samples with high quality. Fuzzy semi-supervised clustering (FSSC) methods achieve a high accuracy with limited labelled samples. Thus, currently, FSSC methods attract more and more attention. This paper proposes a novel method to model the spectral uncertainty for very-high-resolution (VHR) images based on interval type-2 fuzzy sets (IT2 FSs), namely the hierarchical semi-supervising and weighted interval type-2 fuzzy c-means for objects (hierarchical SSW-IT2FCM-O) clustering method. In this method, the VHR image is segmented into image objects to reduce spectral uncertainty within objects. Spectral values, spectral indices and textures were weighted for object-based image classification. To further reduce spectral uncertainty across different objects of the same class, the spectral characteristics of land cover types were represented as banded curves with certain widths instead of precise single-valued spectral curves. The experimental results show that the banded spectral curves produced by the hierarchical SSW-IT2FCM-O can effectively model the spectral uncertainty of geographic objects. From the perspective of classification, four typical validity indices along with the confusion matrix and kappa coefficient were used to test the effectiveness of the hierarchical SSW-IT2FCM-O method, and these indices show that the presented method SSW-IT2FCM-O has greater classification accuracy than the existing FSSC methods and, more importantly, it requires smaller training samples than the existing methods.

scholarly journals INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 385-391
Author(s):  
Chunyan Wang ◽  
Aigong Xu ◽  
Chao Li ◽  
Xuemei Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Fuzzy Model ◽  
Remote Sensing Image ◽  
Fuzzy Membership ◽  
Fuzzy Membership Functions ◽  
Interval Type

Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

scholarly journals Determining the Fuzzifier Values for Interval Type-2 Possibilistic Fuzzy C-means Clustering

2017 ◽  
Vol 27 (2) ◽  
pp. 99-105 ◽  
Author(s):  
Won-Hee Joo ◽  
Frank Chung-Hoon Rhee
Keyword(s):  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Interval Type

Pareto-based interval type-2 fuzzy c-means with multi-scale JND color histogram for image segmentation

2018 ◽  
Vol 76 ◽  
pp. 75-83 ◽  
Author(s):  
Hanqiang Liu ◽  
Feng Zhao ◽  
Vipin Chaudhary
Keyword(s):  
Image Segmentation ◽  
Color Histogram ◽  
Fuzzy C Means ◽  
Multi Scale ◽  
Interval Type
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