Background/Objectives: Image fusion in remote sensing is a challenging task for fusing minute differences in multispectral images for further analysis. The objective of this paper is to propose a hybrid approach for image fusion in remotely sensed images. Methods/Statistical Analysis: The objective of this paper is accomplished by a hybrid approach combining Stationary Wavelet Transform (SWT) and sparse representation. SWT is used for pre-processing and sparse is used for fusing the multitemporal LANDSAT image. Results: Various fusion metrics like RMSE, PSNR and FMI are evaluated and the obtained results show that the proposed hybrid approach outperforms well than the existing methods. The proposed approach gives better results in terms of all the features has been fused correctly, less mean square error and high signal to noise ratio compared to the existing methods such as DWT, SWT and Ehlers. Conclusion/Application: The application of this work is mainly on multispectral multitemporal images. It can be used for change detection also can be used to fuse low resolution image with high resolution remote sensing image. Finally this approach provides an efficient fusion result compared to the traditional methods.

Anisha M Lal

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Anouncia S.M

Kombo O.H.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Indian Journal of Science and Technology

Change detection is the amount of changes that can guide to more concrete understandings into essential method concerning land cover, land usage and ecological variations. This paper deals with an intelligent methodology to optimize the solution out of the solution space that improves the efficiency of the change detection process. To support the theme, an integrated semi-supervised method is designed with a focus on image fusion, semi supervised clustering and binary swarm based optimization. A new approach of handling fusion using sparse coding is referred to expand the amount of information. Using the extracted information, change detection process is carried out by a constrained <a href="https://www.sciencedirect.com/topics/computer-science/clustering-technique">clustering technique</a> to provide a solution reflecting the level of changes occurred in the region of investigation. To improve the accuracy by proposing a global optimum solution, still the result is refined through binary swarm based <a href="https://www.sciencedirect.com/topics/computer-science/process-optimization">optimization process</a> and hence the results are accelerated towards the increased level of accuracy followed by which the change map is reconstructed to show case changes prominently. To determine the accurateness of the proposed methodology quantitative and qualitative analysis has been done with different datasets. The proposed method has been evaluated with existing techniques such as k-means, AKM, FCM, ECKM and ASCC to show the efficiency and proved to be the preeminent change detection methodology compared to the state-of-art methods.

Fulltext

Journal of King Saud University - Computer and Information Sciences

Modernizing the multi-temporal multispectral remotely sensed image change detection for global maxima through binary particle swarm optimization

Change detection is defined as the process that helps in determining the changes associated with land use and land cover properties with reference to georegistered multitemporal remote sensing data. A unique idea of integrating sparse fusion with constrained clustering has been attempted to detect changes in multitemporal and multispectral remote sensing images in a framework called ASCC (to be pronounced as “ask”). Unlike the other traditional methods of change detection, this approach focuses on the fusion of difference images followed by a semisupervised clustering to construct a change detection map in binary form. An enhanced sparse representation is adopted to improve the performance of the fusion process by creating a locally adaptive dictionary that extracts patches from the two difference images. The reconstruction of the fused image is performed using maximum absolute coefficients of the learned dictionary. The resultant fused image is subjected to the change detection process through constrained clustering to discern the changed pixels from the unchanged pixels. To evaluate the designed solution, the method is quantified through percentage correct classification and the process is compared with the existing methods of clustering such as k-means, adaptive k-means, fuzzy C-means, and enhanced constrained k-means. The simulation results demonstrated that the designed approach performed better when compared with state-of-art change detection methods for detecting changes in multitemporal images.

Journal of Applied Remote Sensing

Adapted sparse fusion with constrained clustering for semisupervised change detection in remotely sensed images

In remote sensing, image fusion is the process of blending two images to obtain finer details of the fused image. In this paper, an enhanced dictionary-based sparse representation (EDSR) is proposed for multitemporal image fusion. Multitemporal remote satellite images acquired on the same geographical area at different acquisition dates are merged to obtain a fused image for further analysis. Sparse representation of the image is employed in the approximation and representation of the target image. In order to improve the performance of the fusion process, a locally adaptive dictionary is created such that the dictionary contains patches extracted from both source images. The reconstruction of the image is performed using maximum absolute coefficients through the learned dictionary. The proposed EDSR technique has been compared quantitatively and qualitatively with the existing techniques, such as PCA, DWT, SWT, Ehlers, and sparse representation (SR), to evaluate its performance. The EDSR performs well in mutual information (MI) with 3.4742 and feature mutual information (FMI) with 0.4790 and provides better results in the case of degree of distortion, UIQI, and ERGAS for dataset 1 than the existing fusion methods. Experimental results on LANDSAT images revealed that the proposed technique is more effective in terms of preservation of spectral information, errors, color, and visual quality of the fused product.Keywords:<a href="https://www.tandfonline.com/keyword/Sparse+Representation">Sparse representation</a><a href="https://www.tandfonline.com/keyword/Multitemporal">multitemporal</a><a href="https://www.tandfonline.com/keyword/Image+Fusion">image fusion</a><a href="https://www.tandfonline.com/keyword/Remote+Sensing">remote sensing</a>

European Journal of Remote Sensing

Enhanced Dictionary based Sparse Representation Fusion for Multi-temporal Remote Sensing Images

Change detection is the measure of the thematic change information that can guide to more tangible insights into an underlying process involving land cover, land usage and environmental changes. This paper deals with a semi-supervised change detection approach combining sparse fusion and constrained k means clustering on multi-temporal remote sensing images taken at different timings T1 and T2. Initially a remote sensing fusion method with sparse representation over learned dictionaries is applied to the difference images. The dictionaries are learned from the difference images adaptively. The fused image is calculated by combining the sparse coefficients and the dictionary. Finally the fused image is subjected to constrained k means (CKM) clustering combining few known labelled patterns and unlabelled patterns which have been collected from experts. The enhanced (CKM) approach (ECKM) is compared with k means, adaptive k means (AKM) and fuzzy c means (FCM). Experimental results were carried out on multi-temporal remote sensing images. Results obtained using PCC and F1 measure confirms the effectiveness of the proposed approach. It is also noticed that the ECKM provides better results with less misclassification of errors as compared to k means, adaptive k means and fuzzy c means.

Journal	Indian Journal of Science and Technology
Publisher	Indian Society for Education and Environment
ISSN	0974-6846
Open Access	0