Satellite Image Compression Research Articles

Developed a Method for Satellite Image Compression Using Enhanced Fixed Prediction Scheme

Developed a Method for Satellite Image Compression Using Enhanced Fixed Prediction Scheme

Research on remote sensing satellite image compression and evaluation methods based on GAN

Research on remote sensing satellite image compression and evaluation methods based on GAN

Research on Spaceborne Target Detection Based on Yolov5 and Image Compression

Satellite image compression technology plays an important role in the development of space science. As optical sensors on satellites become more sophisticated, high-resolution and high-fidelity satellite images will occupy more storage. This raises the required transmission bandwidth and transmission rate in the satellite–ground data transmission system. In order to reduce the pressure from image transmission on the data transmission system, a spaceborne target detection system based on Yolov5 and a satellite image compression transmission system is proposed in this paper. It can reduce the pressure on the data transmission system by detecting the object of interest and deciding whether to transmit. An improved Yolov5 network is proposed to detect the small target on the high-resolution satellite image. Simulation results show that the improved Yolov5 network proposed in this paper can detect specific targets in real satellite images, including aircraft, ships, etc. At the same time, image compression has little effect on target detection, so detection complexity can be effectively reduced and detection speed can be improved by detecting the compressed images.

Satellite Image Compression Guided by Regions of Interest

Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform's constraints regarding processing time were analyzed by running the detection algorithms on Unibap's iX10-100 space cloud platform.

Two-dimensional satellite image compression using compressive sensing

Compressive sensing is receiving a lot of attention from the image processing research community as a promising technique for image recovery from very few samples. The modality of compressive sensing technique is very useful in the applications where it is not feasible to acquire many samples. It is also prominently useful in satellite imaging applications since it drastically reduces the number of input samples thereby reducing the storage and communication bandwidth required to store and transmit the data into the ground station. In this paper, an interior point-based method is used to recover the entire satellite image from compressive sensing samples. The compression results obtained are compared with the compression results from conventional satellite image compression algorithms. The results demonstrate the increase in reconstruction accuracy as well as higher compression rate in case of compressive sensing-based compression technique.

Satellite Image Compression and Denoising With Neural Networks

Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveira <i>et al.</i> (2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.

Lossless Compression of Satellite Images using a Versatile Hybrid Algorithm

The remote sensing images taken from various satellites have high resolution and occupy a lot of storage space. It, therefore, becomes difficult to send a greater number of images to the base station. Image compression becomes inevitable in such cases. Satellite image compression decreases repetition in representation of data so that the storage cost and transmission reduces and compensates for the limited on-board resources, in terms of large-scale memory and downlink bandwidth. The aim of this paper is to perform lossless image compression on satellite imagery using a hybrid (DWT-RLE) algorithm to obtain higher compression ratios. This compression is implemented using the software tool MATLAB.

Satellite Image Compression Algorithm Based on the FFT

Satellite Image Compression Algorithm Based on the FFT

Satellite Image Compression Algorithm Based on the FFT

Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level, the reduction in file size allows more images to be stored in a given amount of disk or memory space, it also reduces the time required for images to be sent over the ground. This paper presents a new coding scheme for satellite images. In this study we apply the fast Fourier transform and the scalar quantization for standard LENA image and satellite image. The results obtained after the (SQ) phase are encoded using entropy encoding, after decompression, the results show that it is possible to achieve higher compression ratios, more than 78%, the results are discussed in the paper.

Temporal Gradient based Satellite Image Compression

Temporal Gradient based Satellite Image Compression

Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression

Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes.

An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)-based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.

High efficiency dense light field and all-in-focus compression for lossless satellite image by using CCSDS

Light-field enables us to observe scenes from free view-points. However, it generally consists of 4-D enormous data, which are not suitable for storing or transmitting without effective compression. Image acquisition, transmission, interpretation and visualization have witnessed a lot of research interest and have manifold applications in real life. Light field Images popularly known as Plenoptic Images are light rays travelling in every direction. The data sets generated using plenoptic images are large in size and consume a lot of storage space. This paper studies the general process of image acquisition, light field images and various datasets. Image compression will help the users or professionals to not only achieve storage efficiency but also aid in transmission of Images. In this paper, we study efficient compression of multi-focus images synthesized from dense light-field by using DWT instead of DCT-based compression in order to suppress degradation such as block noise. Quality of reconstructed light-field is evaluated by PSNR for analyzing characteristics of residuals. This paper presents the modeling, design and implementation of two IP cores that are compliant with the CCSDS 121.0-B-2 and CCSDS 123.0-B-1 lossless satellite image compression standards. This paper provides a comparative study of various image compression techniques/algorithms and evaluates them on parameters like peak signal to noise ratio (PSNR), mean square error (MSE), Structural Similarity Index (SSI) and compression ratio (CR).

Data Adaptable Sparse Reconstruction for Hyperspectral Image Recovery from Compressed Measurements

Hyperspectral image compression using compressive sensing technique is very much important in the area of satellite image compression because it can greatly en hance the compression rate. The research work proposes a novel data adaptable sparse reconstruction algorithm for the reconstruction of hyperspectral images from compressive sensing measurements. In the proposed algorithm, compressive sensing technique is used for the compression of HSIs, where Gaussian i.i.d. matrix is used to generate compressive sensing measurements. The algorithm solves the optimization problem containing total variation regularization and data adaptable parameter terms. The regularization terms are added to provide hyperspectral data characteristics as priors into the objective function. The addition of priors helps in convergence of the algorithm into the desired solution. The algorithm alternatively reconstructs the end member matrix and abundance matrix instead of reconstructing the entire HSI at once, thereby reducing the computational complexity at the reconstruction process. To nullify the effect of modelling errors, debiasing is performed.

Data Adaptable Sparse Reconstruction for Hyperspectral Image Recovery from Compressed Measurements

Hyperspectral image compression using compressive sensing technique is very much important in the area of satellite image compression because it can greatly en hance the compression rate. The research work proposes a novel data adaptable sparse reconstruction algorithm for the reconstruction of hyperspectral images from compressive sensing measurements. In the proposed algorithm, compressive sensing technique is used for the compression of HSIs, where Gaussian i.i.d. matrix is used to generate compressive sensing measurements. The algorithm solves the optimization problem containing total variation regularization and data adaptable parameter terms. The regularization terms are added to provide hyperspectral data characteristics as priors into the objective function. The addition of priors helps in convergence of the algorithm into the desired solution. The algorithm alternatively reconstructs the end member matrix and abundance matrix instead of reconstructing the entire HSI at once, thereby reducing the computational complexity at the reconstruction process. To nullify the effect of modelling errors, debiasing is performed.

Implementation of CCSDS Standards for Lossless Multispectral and Hyperspectral Satellite Image Compression

This paper presents the modeling, design, and implementation of two intellectual property (IP) cores that are compliant with the consultative committee for space data systems (CCSDS) 121.0-B-2 and CCSDS 123.0-B-1 lossless satellite image compression standards. The CCSDS 121.0-B-2 describes a lossless universal compressor based on a Rice adaptive encoding. The CCSDS 123.0-B-1 standard describes a lossless algorithm specifically designed for efficient on-board compression of hyperspectral and multispectral images, and it is based on a prediction and entropy-based encoding structure. Two options are offered for the latter: the sample-adaptive and the block-adaptive encoder, which corresponds to the CCSDS 121.0-B-2 algorithm. These IP cores have been designed as independent compressors, but they can be easily combined in a plug-and-play fashion to be used together thanks to a dedicated interface. Additionally, standard interfaces are provided for configuration and external memory access. The design process encompasses the consideration of several different hardware architectures in order to maximize throughput and optimize the requirements of on-board resources at the same time. Both IPs are compliant with the high degree of configurability considered in the standard. The obtained VHDL code is completely technology independent, so it can be used to target any field-programmable gate array (FPGA) or ASIC of interest in the space environment, aiming to perform efficiently compression in satellites despite the inherent constraints of these devices. Results are reported as implementation results for the FPGA devices that are currently considered most representative and suited for on-board use from Xilinx and Microsemi families. In particular, the CCSDS123 IP core reaches for the Virtex5 FX130 a throughput of 153.5 Msamples per second, comprising only 5% of the look-up table (LUTs) in the device.

High Resolution Satellite Image Compression using DCT and EZW

Image Compression has played vital role for efficient image storage, retrieval and transmission. In today’s digital world, images are captured with high wavelength camera lens for better quality, but requires more storage space. Comparison is carried out on multi spectral satellite images using DCT and EZW on basis of compression ratio, PSNR and MSE. DCT performs efficiently at medium bit rates giving compression ratio over 99% and low PSNR whereas EZW performs well at low bit rate giving efficient compression ratio. Disadvantage with DCT is that only spatial correlation of the pixels is considered and the neighborhood correlation is neglected, on the other hand, use of larger EZW wavelet functions produced blurred images.

A comparison of Satellite Image Compression methods in the Wavelet Domain

Better resolution of remotely sensed satellite images will make images clearer and interpretation easier but will increase the total volume of data that has to be managed. In order to reduce data volume for easier satellite communication transmission and reduce the total volume of data needed to be stores, the images should be compressed. Image compression in wavelet domain can be used for both lossy or lossless compression. Four major compression methods are available using the wavelet domain, i.e. CCSDS, Wavelet, Bandelet, and JPEG 2000. Some optical satellite images, were used as input data in simulation software which analyzed and compared the four compression methods in the wavelet domain. The result showed that the CCSDS method yielded the fastest compression and decompression time, but the Bandelet method retained better image quality when reconstructing original images or approximations of them compared to CCSDS. The JPEG 2000 method delivered better quality images than CCSDS for low bit rate. In summary at a rate of 0.25 bpp, CCSDS is 15 times faster than Bandelet and 3 times faster than JPEG2000. However, CCSDS quality is lower by up to 8.77% compared with Bandelet and up to 13.64% compared with JPEG2000.

Band reordering heuristics for lossless satellite image compression with 3D-CALIC and CCSDS

Remote sensing satellite images are used widely in space imaging applications as they collect significant information of ground objects through capturing the ground surface in immense wavelength bands. The size of these images is typically enormous in quantity due to the bulky number of capturing wavelengths. The images need to transmit to the ground from the sensors for a specific application. Thus, the efficient compression techniques are required to fit the available bandwidth for reducing the transmission time. The data in the images are usually redundant spatially, spectrally and temporally which give an ample opportunity to compress the images in various domains. Most importantly, the data features have a strong correlation in the separate spectral area. As a result, the similarity-based band reordering strategy is used to increase the compression performance in comparison to the image having natural band order. However, finding the optimal band reordering is still a computationally challenging problem. In this paper, three different methods namely Band Reordering based on Consecutive Continuity Breakdown Heuristics (BRCCBH), Band Reordering based on Weighted-Correlation Heuristic (BRWCH) and Segmented BRCCBH have been proposed for the compression of multispectral, hyperspectral and hyperspectral sounder data. The presented methods are different on the number and type of heuristics used for obtaining the optimal band reordering. The performances of the proposed band reordering methods are tested using CCSDS 123 lossless predictor and lossless 3D-CALIC. The experimental results show the significant improvement on compression performance by using the proposed band ordering techniques for different types of real multispectral data (3–5% using CCSDS and 2–3% using 3D-CALIC), hyperspectral data (0.2–0.7% using CCSDS and 0.8–1% using 3D-CALIC) and hyperspectral sounder data (5.5–7% using CCSDS and 4–5% using 3D-CALIC).

基于压缩感知理论的卫星云图重构技术研究

压缩感知(Compressive Sensing, CS)理论突破了传统压缩编码技术中奈奎斯特(Nyquist)采样定理的限制，它基于信号的稀疏性、测量矩阵的随机性和非线性优化算法完成对信号的采样压缩和恢复重构。这种全新的理论为有效地克服传统压缩编码技术中的缺陷，解决高分辨率卫星云图压缩中面临的高采样率、大数据量和实时传输等困难提供了可能。本文概述了压缩感知的基本理论，详细探讨了基于压缩感知理论的卫星云图压缩研究，并对压缩感知理论中广泛应用的正交匹配追踪算法进行了优化使其更加适合于卫星云图的处理，并对优化后的算法对卫星云图的重建的效果进行了仿真实验，明确了研究中存在的问题，阐述了下一步的研究方向。 The theory of compressive sensing breaks the limit of traditional SyQuest sampling theory in-cluded traditional compression coding technology. It is based on the sparsity of the signal, the randomness of the measurement matrix and the nonlinear optimization algorithm to complete the sampling compression and reconstruction of the signal. This new theory can effectively overcome the shortcomings of traditional compression coding technology, and solve the difficulties of high sampling rate, large data volume and real-time transmission in high resolution satellite cloud compression. In this paper, the basic theory of compressed sensing is summarized, and the research of satellite image compression based on compressed sensing theory is discussed in detail; The compressed sensing theory is widely used in the orthogonal matching pursuit algorithm, which makes it more suitable for the processing of satellite imagery; The optimized algorithm of satellite cloud image simulation about the reconstruction effect is carried out, clear the problems in the research, and elaborate the research direction of the next step.