scholarly journals FPGA Implementation of Multispectral Image Compression for Satellite Images

2019 ◽  
Vol 8 (9) ◽  
pp. 561-567
Keyword(s):  
Image Compression ◽  
Satellite Images ◽  
Earth Satellite ◽  
Vital Role ◽  
Fpga Implementation ◽  
Multispectral Image ◽  
Complex Wavelet Transform ◽  
Multispectral Data ◽  
The Earth ◽  
Multispectral Image Compression

Multispectral image compression plays a vital role in remote sensing through satellites. Satellite images are more powerful approach to study the space information and research the geographical nature of the earth. Satellite images contains the huge amount of data and it requires more bandwidth for transmission and more memory for storage. Multispectral image compression reduces the size of the multispectral data and makes it easy for storage and transmission to the earth station form the satellite. The image is compressed by reducing the irrelevant and redundant part of data. This paper presents FPGA implementation of multispectral image compression using Dual Tree Complex Wavelet Transform (DTCWT) and Arithmetic Coding. This compression algorithm is implemented and simulated using MATLAB and XILINX ISE14.5 simulator. The FPGA Spartan -6 architecture is used to implement the algorithm. The proposed method gives better result in PSNR and MSE ratio as compared to DWT.

scholarly journals Spectral–Spatial Feature Partitioned Extraction Based on CNN for Multispectral Image Compression

2020 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Fanqiang Kong ◽  
Kedi Hu ◽  
Yunsong Li ◽  
Dan Li ◽  
Shunmin Zhao
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Multispectral Imaging ◽  
Rapid Development ◽  
Spectral Feature ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Spatial Feature ◽  
Spatial Features ◽  
Multispectral Image Compression

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral–spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate.

Spectral Dictionary Learning Based Multispectral Image Compression

2019 ◽  
pp. 140-144
Author(s):  
Wei Liang ◽  
Yinghui Wang ◽  
Wen Hao ◽  
Xiuxiu Li ◽  
Xiuhong Yang ◽  
...  
Keyword(s):  
Image Compression ◽  
Dictionary Learning ◽  
Multispectral Image ◽  
Multispectral Image Compression
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