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Hybrid Spatial-Spectral Autoencoder Models for Lossy satellite image compression

JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第5期22卷 336-357页

作者： Badr, Mohamed Ahmed Elrewainy, Ahmed Fathy Elshafey, Mohamed Abdelmoneim Taha Mil Tech Coll Avion Engn Dept Elect Engn Branch Cairo 11765 Egypt Mil Tech Coll Comp Engn & Artificial Intelligence Dept Elect Engn Branch Cairo 11765 Egypt

The challenge of handling vast amounts of high-resolution satellite imagery is driven by onboard memory and bandwidth limitations. As spatial and spectral resolutions increase, image compression, particularly deep-learning-based methods, is essential to overcome these limitations. This paper presents hybrid autoencoder models that combine convolutional neural networks, long short-term memory networks, and attention mechanisms for spatial and spectral feature extraction. The proposed architectures, including sparse and variational autoencoder counterparts, form a comprehensive image compression framework with quantization, and various entropy coders are applied to the EuroSat dataset (RGB and multispectral). The experimental results show the models' superiority over the JPEG family and recent state-of-the-art methods, achieving up to 3.3, 1.4, and 0.6% improvements in the peak signal-to-noise ratio, structural similarity index, and multiscale structural similarity index, respectively. Moreover, performance analysis in terms of computational complexity, processing time, and memory usage highlights the efficiency of the proposed models. A case study conducted on a real scene from the Sentinel-2 satellite further validates the compatibility of the proposed models with modern artificial intelligence chipsets.

关键词： Convolutional Neural Network Long Short-Term Memory Attention mechanisms Spatial Spectral Feature Extraction AI-Chipsets Compatibility Computational Complexity Performance satellite image compression Hybrid Deep Learning Models Earth Observation satellite Technology Smart satellites

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Optimal Quantization Table Generation for Efficient satellite image compression Using Teaching Learning Based Optimization Technique 1

Optimal Quantization Table Generation for Efficient Satellit...

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International Conference on Big Data Analytics and Computational Intelligence (ICBDAC)

作者： Thakur, Vikrant Singh Gupta, Shubhrata Thakur, Kavita Natl Inst Technol Raipur Dept Elect Engn Raipur Madhya Pradesh India Pt Ravishankar Shukla Univ SOS Elect & Photon Raipur Madhya Pradesh India

ISBN: (纸本)9781509064007

Communication of the satellite images plays a crucial role in many important applications such as change detection, land cover classification, weather prediction etc. The increase in the demand of satellite images over the band limited channel requires good quality compression tools. Currently, the compression standard developed by Joint Photographic Experts Group (JPEG) is the widely used product for the compression of satellite images. Moreover, one of the important elements which influence the performance of JPEG compression standard is the nature of quantization table. The compression ratio and the decompressed image quality arc determined all-together by the quantization table, and hence, the table strongly influences the whole compression performance. The author aims to generate better quantization tables to enhance the compression performance to achieve higher compression ratios while preserving high reconstruction quality for satellite images. A Teaching Learning based Optimization (TLBO) technique is employed to promote higher compression performance for satellite images. The aim is to identify optimal quantization tables that contribute to better compression efficiency in terms of image quality indexes Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The TLBO based optimization strategy is proposed because it is free from the algorithmic parameters. Experiments were carried out on the three different satellite images. The results demonstrate that the identified optimal quantization tables offer average PSNR gain of about 0.5 dB with the average reduction in MSE of about 10 against the available JPEG quantization table.

关键词： satellite image compression JPEG quantization table TLBO optimization algorithm compression ratio PSNR MSE

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satellite multispectral image compression based on removing sub-bands

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OPTIK 2017年 131卷 1023-1035页

作者： Hagag, Ahmed Hassan, Emad S. Amin, Mohamed Abd El-Samie, Fathi E. Fan, Xiaopeng Harbin Inst Technol Dept Comp Sci & Technol Harbin 150001 Peoples R China Egyptian E Learning Univ Fac Informat Technol Dept Informat Technol Giza 12611 Egypt Menoufia Univ Dept Elect & Elect Commun Fac Elect Engn Menoufia 32952 Egypt Menoufia Univ Dept Math Fac Sci Shibin Al Kawm 32511 Egypt

This paper presents an efficient technique for satellite multispectral image compression aiming at reducing the size of storage of multispectral images with high-quality reconstruction. The proposed technique is based on removing sub-bands before compression. The removed sub-bands are determined using the correlation coefficients between bands. In the compression process, we use the Discrete Wavelet Transform (DWT) followed by an entropy coder (e.g., a Huffman or an arithmetic encoder) for the most correlated bands. Moreover, we use JPEG2000 to compress the rest of bands with Principal Component Analysis (PCA) as a spectral decorrelation transform. Enhanced Thematic Mapper plus (ETM+) satellite multispectral images are used for the validation of the proposed technique. Experiments results demonstrate that the proposed technique improves the average multispectral image quality by 3-11 dB. The experimental results verify the effectiveness of the proposed technique. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： satellite image compression Landsat ETM Removing sub-bands JPEG2000 PCA DWT

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In-Memory-Computing (IMC) Technique in Local Difference Decision Block of an On-Board satellite Hyperspectral Data compression Algorithm 66

In-Memory-Computing (IMC) Technique in Local Difference Deci...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Joshi, Vijay Rani, Sheeba J.

ISBN: (纸本)9798350302103

Due to high data acquisition rates and large data volumes, on-board hyperspectral imagery (HSI) demands data compression before transmission. The major bottleneck in on-board implementation of algorithms is the limited availability of power and resources along with the need to be less error prone while accessing and processing the data. The recently proposed simple lossless algorithm (SLA) for on-board satellite hyperspectral data compression, performs 80% calculations by utilizing previous neighborhood pixels, which in turn requires majority of memory access. This may lead to an increase in overall power dissipation and probability of error due to bit flips in the von-Neumann architectures of SLA. Thus, to reduce the probability of error and to improve power efficiency, this work adopts in-memory-computing (IMC) technique for the local difference (LD) decision block of the SLA for non-overlapping case. The LD decision block calculates absolute difference between the neighboring pixels and requires XOR operation for the same. To perform IMC, the SRAM cell from literature is modified to get XOR between two pixels. A non-overlapping (6*16) bit memory cell is implemented using von-Neumann and IMC based architecture for LD decision block. The proposed architecture allows reduced memory overheads and instead of using more number of transistors in a cell, consumes comparable power when compared to the von-Neumann architecture. The error resilience of SLA is expected to improve, as the total memory access for processing a pixel is reduced by 21.42%. © 2023 IEEE.

关键词： hyperspectral data IMC on-board data compression satellite image compression XOR based SRAM cell

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Evaluating the Quality of compression in Very High Resolution satellite images Using Different compression Methods

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INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA 2015年 19卷 91-102页

作者： Sanjith, S. Ganesan, R. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumarcoil Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Elect & Instrumentat Kumarcoil Tamil Nadu India

The rapid growth of remote sensing technology has a great advantage in producing high resolution images which are huge in data volume. Due to the huge volume it is tedious to store and transmit the data. In order to overcome this, a good compression algorithm should be used to compress the data before storing are transmitting. In this paper we have chosen seven different very high resolution satellite images namely Worldview 3, Worldview 2, GeoEye-1, Worldview 1, Pleiades, Quick Bird and IKONOS they are compressed using three different compression methods JPEG, SPIHT and JPEG2000. The Mean square Error, Signal to noise Ratio and Peak Signal to Noise Ratio are calculated to evaluate the quality of the compression methods in very high resolution satellite images.

关键词： Mean Square Error (MSE) Signal to Noise Ratio (SNR) Peak Signal to Noise Ratio (PSNR) JPEG SPIHT JPEG2000 satellite image compression

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Determining the Quality of compression in High Resolution satellite images Using Different compression Methods

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INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA 2015年 20卷 202-217页

作者： Sanjith, S. Ganesan, R. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumarcoil Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Elect & Instrumentat Kumarcoil Tamil Nadu India

Measuring the quality of image is very complex and hard process since the opinion of the humans are affected by physical and psychological parameters. So many techniques are invented and proposed for image quality analysis but none of the methods suits best for it. Assessment of image quality plays an important role in image processing. In this paper we present the experimental results by comparing the quality of different satellite images (ALOS, RapidEye, SPOT4, SPOT5, SPOT6, SPOTMap) after compression using four different compression methods namely Joint Photographic Expert Group (JPEG), Embedded Zero tree Wavelet (EZW), Set Partitioning in Hierarchical Tree (SPIHT), Joint Photographic Expert Group - 2000 (JPEG 2000). The Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) values are calculated to determine the quality of the high resolution satellite images after compression.

关键词： Square Error (MSE) Signal to Noise Ratio (SNR) Peak Signal to Noise Ratio (PSNR) JPEG EZW SPIHT JPEG2000 satellite image compression

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An enhanced Wavelet-based scheme for near lossless satellite image compression

An enhanced Wavelet-based scheme for near lossless satellite...

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Conference on Applications of Digital image Processing XXXII

作者： Lin, Tsung-Ching Chen, Chien-Wen Chen, Shi-Huang Truong, Trieu-Kien I Shou Univ Dept Informat Engn Kaohsiung 840 Taiwan Shu Te Univ Dept Comp Sci & Informat Engn Kaohsiung 824 Taiwan

ISBN: (纸本)9780819477330

An enhanced wavelet-based compression scheme for satellite image is proposed in this paper. The Consultative Committee for Space Data System (CCSDS) presented a recommendation which utilizes the wavelet transform and the bit plane coder for satellite image compression. The bit plane coder used in the CCSDS recommendation encodes the coefficient block of bit planes one by one and then truncates the unnecessary bit plane coefficient blocks. By this way, the contexts of bit planes are not considered as the redundancy embedded data which may be compressed further. The proposed scheme uses a bit plane extractor to parse the differences of the original image data and its wavelet transformed coefficients. The output of bit plane extractor will be encoded by a run-length coder and will be sent to the communication channel with the CCSDS compressed data. Comparing with the recommendation of CCSDS, under a reasonable complexity, the subjective quality of the image will maintained or even better. In addition, the bit-rate can be further decreased from 85% to 95% of the CCSDS image compression recommendation at the similar objective quality level. By using the lower bit rate lossy mode compression and bit plane compensation, it is possible to obtain lower bit rate and higher quality image than which the higher bit rate lossy mode compression can achieve.

关键词： satellite image compression Wavelet-based compression Bit plane compensation

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The study of enhanced earth observations on a satellite image chain

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ADVANCES IN SPACE RESEARCH 2011年第8期48卷 1371-1376页

作者： Yong, Sang-Soon Choi, Myungjin Ra, Sung-Woong Korea Aerosp Res Inst KOMPSAT Program Off 3 Taejon 305600 South Korea Korea Aerosp Res Inst Satellite Informat Res Inst Taejon 305600 South Korea Chungnam Natl Univ Dept Elect Engn Taejon 305600 South Korea

The Multi-Spectral Camera (MSC) on the KOrea Multi-Propose satellite (KOMPSAT)-2 was developed and launched as a main payload to provide a One(1) m panchromatic image and four(4) band four(4) m multi-spectral images at an altitude of 685 km covering a swath width of 15 km. These images, archived around the world, are a useful resource for space applications in agriculture, cartography, geology, forestry, regional planning, surveillance, and national security. The image quality of KOMPSAT-2 depends upon its image chain, which is comprised of an on-board system in the satellite and a processing system at the ground station. Therefore, in this study we determine the factors that have a major impact on the image quality through an investigation of the entire image chain. Consequently, two methods, involving a compression algorithm and a deconvolution technique, were determined as having a significant influence on the KOMPSAT-2 image quality. The compression algorithm of KOMPSAT-2 is rate-controlled JPEG-like algorithm that controls the mismatch between the input and output data rate. The ability to control the input/output data rate may be useful during the operation of the satellite but can also lower the overall image quality. The deconvolution technique may increase the sharpness of images, but it can also amplify the image noise level. Therefore, we propose methods of wavelet-based compression and denoising as an alternative to currently existing algorithms. Satisfactory results were obtained through experimentation with these two algorithms, and they are expected to be successfully implemented into the future KOMPSAT series to yield high-quality images for enhanced earth observation. (C) 2011 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： KOMPSAT MSC satellite image compression satellite image deconvolution image chain image quality

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