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SAR Image Fusion Using Fire Fly Algorithm

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Abstract:

Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyper spectral images(HSI) is limited, due to sensor limitations. In many applications,HSI with a high spectral as well as spatial resolution are required. In this paper, a new method for spatial resolution enhancement of a HSI using spectral unfixing and sparse coding (SUSC) is introduced. The proposed method fuses high spectral resolution features from the HSI with high spatial resolution features from anMSI of the same scene. End members are extracted from the HIS by spectral unmixing, and the exact location of the endmembersis obtained from the MSI. This fusion process by using spectral unmixing is formulated as an ill-posed inverse problem which requires a regularization term in order to convert it into a wellposedinverse problem. As a regularize, we employ sparse coding(SC), for which a dictionary is constructed using high spatial resolution MSI or PAN images from unrelated scenes. The proposedalgorithm is applied to real Hyperion and ROSIS datasets.Compared with other state-of-the-art algorithms based on pansharpening, spectral unmixing, and SC methods, the proposedmethod is shown to significantly increase the spatial resolution while preserving the spectral content of the HSI.for this we are using firefly algorithm.

Keywords: firefly algorithm, robust feature extraction, vegetation images, shadow images, multi spectral images

 

Introduction:

Image fusion is a process, which creates a new image representing combined information composed from two or more source images. Generally, one aims to preserve as much source information as possible in the fused image with the expectation that performance with the fused image will be better than, or at least as good as, performance with the source images . Image fusion is only an introductory stage to another task, e.g. human monitoring and classification. Therefore, the performance of the fusion algorithm must be measured in terms of improvement or image quality. Several authors describe different spatial and spectral quality analysis techniques of the fused images. Some of them enable subjective, the others objective, numerical definition of spatial or spectral quality of the fused data .The evaluation of the spatial quality of the pansharpened images is equally important since the goal is to retain the high spatial resolution of the PAN image. A survey of the pan sharpening literature revealed there were very few papers that evaluated the spatial quality of the pan-sharpened imagery . Consequently, there are very few spatial quality metrics found in the literatures. However, the jury is still out on the benefits of a fused image compared to its original images. There is also a lack of measures for assessing the objective quality of the spatial resolution of the fusion methods. Therefore, an objective quality of the spatial resolution assessment for fusion images is required. Therefore, this study presented a new approach to assess the spatial quality of a fused image based on High pass Division Index (HPDI). In addition, many spectral quality metrics, to compare the properties of fused images and their ability to preserve the similarity with respect to the original MS image while incorporating the spatial resolution of the PAN.

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Existing Method:

  • Averagingand Maximization methods based spatial level fusion
  • Thresholding and K means clustering methods for segmentation:
  • Wavelet Transform

 

Drawbacks:

  • Contrast information loss due to averaging method
  • Maximization method sensitive to sensor noise and high spatial distortion
  • K means – It is not suitable for all lighting condition of images
  • Difficult to measure the cluster quality

 

 Proposed Method:

  • Dual Level Wavelet and Log Ration Transform
  • Detection of Back scattering Changes at Building Scale
  • Building Detection using NN classifier.

 

Block Diagram:

SAR Image Fusion Using Fire Fly Algorithm

 

Advantages:

  • Accurate detection of foreground changes by fusion
  • Less sensitive to noises

 

 

Applications:

  • Earth land changes detection in Satellite field
  • Medical field.

 

 

Conclusion:

Finally, using this hearth fly algorithm we will get the precise area of the building. For this purpose take input images, multi spectral images and dynamic images. Using this we are becoming fusion image. Then getting different outputs after fusion. That won’t to get the precise area of the buildings. For that we are using robust feature extraction, measurement areas, and shadow of the pictures, vegetation images, detection process and building masking. And that we are saying pan images is nothing but the fusion based thing. To detect and locate changes between SAR images from the satellite we are using this project. During this project we will expect good results by using fire fly algorithm. For that purpose we will approach these techniques.

 

References:

[1] S. Dasgupta, C. F. Stevens, and S. Navlakha, A neural algorithm for afunda-mental computing problem, Science, vol. 358, no. 6364, pp.793-796, 2017.

[2] X. Tang, L. Jiao, and W. J. Emery, SAR Image Content Retrieval Basedon Fuzzy Similarity and Relevance Feedback, IEEE Journal of SelectedTopics in Applied Earth Observations and Remote Sensing, vol. 10, no. 5,pp. 1824-1842, 2017.

[3] L. Jiao, X. Tang, B. Hou, et al., SAR images retrieval based onsemantic classification and region-based similarity measure for earthobservation, IEEE Journal of Selected Topics in Applied EarthObservations and Remote Sensing, vol. 8, no. 8, pp. 3876-3891, 2017.

[4] X. Tang and L. Jiao, Fusion Similarity-Based Reranking for SARImage Retrieval, IEEE Geoscience and Remote Sensing Letters, vol. 14,no. 2, pp. 242-246, 2017.

[5] M. Datcu, H. Daschiel, A. Pelizzari, et al., Information mining inremote sensing image archives: System concepts, IEEE Transactions onGeo-science and Remote Sensing, vol. 41, no. 12, pp. 2923-2936, 2017.

[6] Y. Yang and S. Newsam, Geographic image retrieval using localinvariant features, IEEE Transactions on Geoscience and Remote Sensing,vol. 51, no. 2, pp. 832, 2016.

[7] M. Ferecatu and N. Boujemaa, Interactive remote-sensing imageretrieval using active relevance feedback, IEEE Transactions onGeoscience and Remote Sensing, vol. 45, no. 4, pp. 818-826, 2007.

[8] B. Demir and L. Bruzzone, Hashing-based scalable remote sensingimage search and retrieval in large archives, IEEE Transactions onGeoscience and Remote Sensing, vol. 54, no. 2, pp. 892-904, 2016.

[9]M. S. Charikar, Similarity estimation techniques from roundingalgo-rithms, ACM symposium on Theory of computing, pp. 380-388,2018.

  1. Indyk and R. Motwani, Approximate nearest neighbors: towardsremov-ing the curse of dimensionality, ACM symposium onTheory of computing, pp. 604-613, 2018.

[11] J. Wang, T. Zhang, N. Sebe, et al. A survey on learning to hash, IEEETransactions on Pattern Analysis and Machine Intelligence, 2017.

[12] R. Salakhutdinov and G. Hinton, Semantic hashing, InternationalJournal of Approximate Reasoning, vol. 50, no. 7, pp. 969-978, 2018.

[13] Y. Weiss, A. Torralba, and R. Fergus, Spectral hashing, Advances inNeural Information Processing Systems, pp. 1753-1760, 2019.

[14] Q. Li, Z. Sun, R. He, et al. Deep Supervised Discrete Hashing, Advancesin Neural Information Processing Systems, 2017.

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