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Sharifi, A.. (1398). Using Convolutional Sparse Representation and Discrete Wavelet Decomposition for Satellite Image Pan-sharpening. فناوری آموزش, 7(2), 205-212. doi: 10.22061/jecei.2020.6297.296
A. Sharifi. "Using Convolutional Sparse Representation and Discrete Wavelet Decomposition for Satellite Image Pan-sharpening". فناوری آموزش, 7, 2, 1398, 205-212. doi: 10.22061/jecei.2020.6297.296
Sharifi, A.. (1398). 'Using Convolutional Sparse Representation and Discrete Wavelet Decomposition for Satellite Image Pan-sharpening', فناوری آموزش, 7(2), pp. 205-212. doi: 10.22061/jecei.2020.6297.296
Sharifi, A.. Using Convolutional Sparse Representation and Discrete Wavelet Decomposition for Satellite Image Pan-sharpening. فناوری آموزش, 1398; 7(2): 205-212. doi: 10.22061/jecei.2020.6297.296

Using Convolutional Sparse Representation and Discrete Wavelet Decomposition for Satellite Image Pan-sharpening

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 8، دوره 7، شماره 2، مهر 2019، صفحه 205-212    اصل مقاله (1.06 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2020.6297.296
نویسنده
A. Sharifi*
Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
تاریخ دریافت: 27 آبان 1397،  تاریخ بازنگری: 18 اسفند 1397،  تاریخ پذیرش: 21 اردیبهشت 1398 
چکیده
Background and Objectives: High resolution multi-spectral (HRMS) images are essential for most of the practical remote sensing applications. Pan-sharpening is an effective mechanism to produce HRMS image by integrating the significant structural details of panchromatic (PAN) image and rich spectral features of multi-spectral (MS) images.
Methods: The traditional pan-sharpening methods incur disadvantages like spectral distortion, spatial artifacts and lack of details preservation in the fused image. The pan-sharpening approach proposed in this paper is based on integrating wavelet decomposition and convolutional sparse representation (CSR). The wavelet decomposition is performed on PAN and MS images to obtain low-frequency and high-frequency bands. The low-frequency bands are fused by exploring the CSR based activity level measurement.
Results: The HRMS image is restored by implementing the inverse transform on fused bands. The fusion rules are constructed, thus to transfer the crucial details from source images to the fused image effectively.
Conclusion: The proposed method produces HRMS images with rational spatial and spectral qualities. The visual outcomes and quantitative measures approve the eminence of the proposed fusion framework.
کلیدواژه‌ها
Remote Sensing؛ Image fusion؛ Pansharpening؛ Discrete wavelet transform؛ Convolutional Sparse representation
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آمار
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