Multispectral Image Compression for various band images with High Resolution Improved DWT SPIHT

Raju, V. Bhagya; Sankar, K. Jaya; Naidu, C. D.; Bangalore, Srinivas

doi:10.14257/ijsip.2016.9.2.23

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…Satellite images usually have high resolution since they cover large areas of the earth's surface [10]. Hence, image compression is needed to decrease the bandwidth required to transmit data from the satellite to the ground where the image is reconstructed while maintaining the reconstructed image quality as much as possible to preserve its scientific value.…”

Section: Image Compressionmentioning

confidence: 99%

Image Compression Techniques on-Board Small Satellites

Shihab

2023

Iraqi Journal of Science

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Raw satellite images are considered high in resolution, especially multispectral images captured by remote sensing satellites. Hence, choosing the suitable compression technique for such images should be carefully considered, especially on-board small satellites, due to the limited resources. This paper presents an overview and classification of the major and state-of-the-art compression techniques utilized in most space missions launched during the last few decades, such as the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT)-based compression techniques. The pros and cons of the onboard compression methods are presented, giving their specifications and showing the differences among them to provide unified information about these methods for researchers and satellite imaging payload designers. Hence, some of these techniques are implemented, and comparisons are presented in the current work as examples to simulate an image compression system on board a small satellite using the MATLAB software package. This was achieved by employing three LANDSAT8, band6 satellite images. A wavelet selection was also considered for the DWT-based compression method, which gave the best results among the other methods through acquiring high values of compression ratio (CR) while maintaining the important scientific information of the image when reconstructed at the ground station.

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Section: Image Compressionmentioning

confidence: 99%

Image Compression Techniques on-Board Small Satellites

Shihab

2023

Iraqi Journal of Science

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“…Finally, a fused image is formed using the corresponding inverse transform. Mathematical transforms, which are well known as Multiscale Geometric Analyses (MGAs) include the Discrete Wavelet Transform (DWT) [37][38][39], Shearlet Transform (SHT) and Discrete Cosine Transform-based Laplacian Pyramid (DCT-LP). The DWT generates four different coefficients: approximation, horizontal detail, vertical detail and diagonal detail, with a mean-maximum rule applied to fuse those of multiple spectra.…”

Section: Fusionmentioning

confidence: 99%

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

2020

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Multispectral Polarimetric Imagery (MSPI) contains significant information about an object’s distribution, shape, shading, texture and roughness features which can distinguish between foreground and background in a complex scene. Due to spectral signatures being limited to material properties, Background Segmentation (BS) is a difficult task when there are shadows, illumination and clutter in a scene. In this work, we propose a two-fold BS approach: multiband image fusion and polarimetric BS. Firstly, considering that the background in a scene is polarized by nature, the spectral reflectance and correlations and the textural features of MSPI are calculated and analyzed to demonstrate the fusion significance. After that, integrating Principal Component Analysis (PCA) with Fast Fourier Transform (FFT), a hybrid fusion technique is proposed to show the multiband fusion effectiveness. Secondly, utilizing the Stokes vector, polarimetric components are calculated to separate a complex scene’s background from its foreground by constructing four significant foreground masks. An intensity-invariant mask is built by differentiating between the median filtering versions of unpolarized and polarized images. A strongly unpolarized foreground mask is also constructed in two different ways, through analyzing the Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP). Moreover, a strongly polarized mask and a strong light intensity mask are also calculated based on the azimuth angle and the total light intensity. Finally, all these masks are combined, and a morphological operation is applied to segment the final background area of a scene. The proposed two-fold BS algorithm is evaluated using distinct statistical measurements and compared with well-known fusion methods and BS methods highlighted in this paper. The experimental results demonstrate that the proposed hybrid fusion method significantly improves multiband fusion quality. Furthermore, the proposed polarimetric BS approach also improves the mean accuracy, geometric mean and F1-score to 0.95, 0.93 and 0.97, respectively, for scenes in the MSPI dataset compared with those obtained from the methods in the literature considered in this paper. Future work will investigate mixed polarized and unpolarized BS in the MSPI dataset with specular reflection.

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“… Shichao Zhou et al [10] introduced a review of recently remote sensing image compression and dividing these methods into: predictive coding, transform coding, Region Of Interest (ROI)-based compression methods and taskdriven coding methods.  V. Bhagya Raju et al [11] submitted an improved Set Partitioning in Hierarchical Tree (SPIHT) algorithm with 2D -Discrete wavelet transforms(DWT) for multispectral image compression for various band images with high resolution  LIU XiJia et al [12] Implemented a novel remote-sensing image compression, based on using priori-information collection of historical remote-sensing images and registration technique to remove temporal correlation between newly-captured remote-sensing images and historical ones. The results outperform JPEG2000 and JPEG by over 1.37 times for lossless compression.…”

Section: Related Workmentioning

confidence: 99%

Lossy Compression for Raw Satellite Images Using Intra Frame and Inter Frame Coding

2017

IJSR

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Abstract:The huge size of the satellite images data leads to big problems in storage capacity and power exhaustion for the transmission, therefore great efforts have been made to find low-complexity compression algorithms. To overcome these issues in this field we have proposed satellite image compression method based on inter-intra frame coding, to compress sequence of satellite images captured at different times, form different sensors, or /and different view points for the same area. Inter-frame coding principle based on removing temporal redundancy exists between two successive satellite images: a historical captured image (reference image) and a new captured image (sensed image) for the same scene. Since sequential satellite images contain almost same materials with only minor movements according to the time interval between them (several days, several months). The resulting difference's image after subtraction has much lower entropy and can be compressed with much less cost than the original sensed satellite image. This is followed by removing still existing redundancy of pixels within resulting difference's image using principle intra-frame coding, and this will be helpful in order to satisfy high compression performance while keeping the quality of the compressed satellite image as much as possible.

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Multispectral Image Compression for various band images with High Resolution Improved DWT SPIHT

Cited by 12 publications

References 10 publications

Image Compression Techniques on-Board Small Satellites

Image Compression Techniques on-Board Small Satellites

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

Lossy Compression for Raw Satellite Images Using Intra Frame and Inter Frame Coding

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