Journal ArticleDOI
Performance Comparison of Wavelet and Contourlet Frame Based Features for Improving Classification Accuracy in Remote Sensing Images
TLDR
In this paper, different contourlet frame based feature extraction techniques for remote sensing images are proposed Principal component analysis (PCA) method is used to reduce the number of features Gaussian Kernel fuzzy C-means classifiers uses these features to improve the classification accuracy Accuracy assessment based on field visit data and cluster validity measures are used to measure the accuracy of the classified data.Abstract:
Conventional classification algorithms makes the use of only multispectral information in remote sensing image classification Wavelet provides spatial and spectral characteristics of a pixel along with its neighbours and hence this can be utilized for an improved classification The major disadvantage of wavelet transform is the non availability of spatial frequency features in its directional components The contourlet transform based laplacian pyramid followed by directional filter banks is an efficient way of extracting features in the directional components In this paper different contourlet frame based feature extraction techniques for remote sensing images are proposed Principal component analysis (PCA) method is used to reduce the number of features Gaussian Kernel fuzzy C-means classifiers uses these features to improve the classification accuracy Accuracy assessment based on field visit data and cluster validity measures are used to measure the accuracy of the classified data The experimental result shows that the overall accuracy is improved to 173 % (for LISS-II), 181 % (for LISS-III) and 195 % (for LISS-IV) and the kappa coefficient is improved to 0933 (for LISS-II), 00103 (for LISS-III) and 00214 (for LISS-IV) and also the cluster validity measures gives better results when compared to existing methodread more
Citations
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Journal ArticleDOI
A deep heterogeneous feature fusion approach for automatic land-use classification
A Fotso Kamga Guy,A Fotso Kamga Guy,Tallha Akram,Bitjoka Laurent,Syed Rameez Naqvi,Mengue Mbom Alex,Nazeer Muhammad +6 more
TL;DR: A novel hybrid system for satellite image classification that combines the distinct information of deep features, and generate a discriminative representation by preserving the essential information of original feature space is proposed.
Journal ArticleDOI
Improved segmentation and change detection of multi-spectral satellite imagery using graph cut based clustering and multiclass SVM
TL;DR: The innovative spatial-spectral method for image segmentation and change detection based on Graph cut based clustering is proposed, and it is observed that in the proposed work, the mean value of the changed area for a particular dataset achieves a 47.2% reduction compared to the conventional system.
Journal ArticleDOI
Refining Training Samples Using Median Absolute Deviation for Supervised Classification of Remote Sensing Images
Xunqiang Gong,Li Shen,Tieding Lu +2 more
TL;DR: Refining training samples using the median absolute deviation (MAD) can effectively eliminate the influence of impure training samples so that the more reliable and accurate results can be obtained.
Journal ArticleDOI
3-D Shearlet Transform Based Feature Extraction for Improved Joint Sparse Representation HSI Classification
TL;DR: This paper aims at exploiting shearlet 3D to highlight the intrinsic properties of hyperspectral images (HSIs), well known by their correlated information and high dimensionality, by building discriminative descriptors reaching high overall accuracies for two different HSI datasets, without taking into account all the shearlett 3D coefficients.
Book ChapterDOI
Prediction-Based Lossless Image Compression
Mohamed Uvaze Ahamed Ayoobkhan,Eswaran Chikkannan,Kannan Ramakrishnan,Saravana Balaji Balasubramanian +3 more
TL;DR: A novel classifier which makes use of wavelet and Fourier descriptor features is employed to achieve better compression performance and the compression ratio values achieved are higher compared to those obtained by the known algorithms.
References
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Book
Remote sensing and image interpretation
TL;DR: In this article, the authors present a textbook for introductory courses in remote sensing, which includes concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; air photo interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.
Journal ArticleDOI
Remote Sensing and Image Interpretation
TL;DR: In this article, the concept of remote sensing elements of photogrammetry was introduced. Butterfly, thermal, and hyperspectral sensors were used to interpret multispectral, thermal and hypererspectral images.
Journal ArticleDOI
The contourlet transform: an efficient directional multiresolution image representation
Minh N. Do,Martin Vetterli +1 more
TL;DR: A "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information is pursued and it is shown that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves.
Journal ArticleDOI
Cluster validity methods: part I
TL;DR: This paper presents a review of the clustering validity and methods based on external and internal criteria and discusses the cluster validity approaches based on internal and external criteria.
Journal ArticleDOI
Clustering validity checking methods: part II
TL;DR: The paper illustrates the issues that are under-addressed by the recent approaches to clustering validity checking approaches and proposes the research directions in the field.