A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

Nonnegative tensor Tucker decomposition (NTD) in a transform domain (e.g., 2D-DWT, etc) has been used in the compression of hyper-spectral images because it can remove redundancies between spectrum bands and also exploit spatial correlations of each band. However, the use of a NTD has a very high computational cost. In this paper, we propose a low complexity NTD-based compression method of hyper-spectral images. This method is based on a pair-wise multilevel grouping approach for the NTD to overcome its high computational cost. The proposed method has a low complexity under a slight decrease of the coding performance compared to conventional NTD. We experimentally confirm this method, which indicates that this method has the less processing time and keeps a better coding performance than the case that the NTD is not used. The proposed approach has a potential application in the loss compression of hyper-spectral or multi-spectral images

Hyperspectral (HS) image is a three dimensional data image where the 3rd dimension carries the wealth of spectrum information. HS image compression is one of the areas that has attracted increasing attention for big data processing and analysis. HS data has its own distinguishing feature which differs with video because without motion, also different with a still image because of redundancy along the wavelength axis. The prediction based method is playing an important role in the compression and research area. Reflectance distribution of HS based on our analysis indicates that there is some nonlinear relationship in intra-band. The Multilayer Propagation Neural Networks (MLPNN) with backpropagation training are particularly well suited for addressing the approximation function. In this paper, an MLPNN based predictive image compression method is presented. We propose a hybrid Adaptive Prediction Mechanism (APM) with MLPNN model (APM-MLPNN). MLPNN is trained to predict the succeeding bands by using current band information. The purpose is to explore whether MLPNN can provide better image compression results in HS images. Besides, it uses less computation cost than a deep learning model so we can easily validate the model. We encoded the weights vector and the bias vector of MLPNN as well as the residuals. That is the only few bytes it then sends to the decoder side. The decoder will reconstruct a band by using the same structure of the network. We call it an MLPNN decoder. The MLPNN decoder does not need to be trained as the weights and biases have already been transmitted. We can easily reconstruct the succeeding bands by the MLPNN decoder. APM constrained the correction offset between the succeeding band and the current spectral band in order to prevent HS image being affected by large predictive biases. The performance of the proposed algorithm is verified by several HS images from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) reflectance dataset. MLPNN simulation results can improve prediction accuracy; reduce residual of intra-band with high compression ratio and relatively lower bitrates.

Hyperspectral image compression has become increasingly important in data exploitation because of enormous data volumes and high redundancy provided by hundreds of contiguous spectral channels. Since a hyperspectral image can be viewed as a 3-dimensional (3D) image cube, many efforts have been devoted to extending 2D image compression techniques to perform 3D image compression on hyperspectral image cubes. Unfortunately, some major issues generally encountered in hyperspectral data exploitation at low or very low-bit rate compression, for example, subpixels and mixed pixels which do not occur in traditional pure pixel-based image compression are often overlooked in such a 2D-to-3D compression. Accordingly, a direct application of 2D-to-3D compression techniques to hyperspectral image cubes without taking precaution may result in significant loss of crucial spectral information provided by subtle substances such as small objects, anomalies during low bit-rate lossy compression. This paper takes a rather different view by investigating lossy hyperspectral compression from a perspective of exploring spectral information, referred to as exploitation-based lossy compression and further develops spectral/spatial hyperspectral image compression to effectively preserve crucial and vital spectral information of objects which are generally missed by commonly used mean-squared error (MSE) or signal-to-noise ratio (SNR)-based compression techniques when lossy compression is performed at low bit rates. In order to demonstrate advantages of the proposed spectral/spatial compression approach applications of subpixel target detection and mixed pixel analysis are used for experiments for performance evaluation.

Linear unmixing-based compressive sensing has been extensively exploited for hyperspectral image (HSI) compression in recent years among which gradient sparsity is widely used to characterize the spatial continuity of abundance matrix given a small amount of endmembers. Though these methods have achieved good reconstruction results, identifying necessary endmembers from an HSI is challenging for them. In this study, instead of using a small amount of given endmembers, a locally similar sparsity-based hyperspectral unmixing compressive sensing (LSSHUCS) method is proposed to unmix the HSI with an established redundant endmember library. Considering that each pixel is a mixture of several endmembers, a novel locally similar sparsity constraint is imposed on the abundance matrix, which depicts the sparsity of abundance vectors and the local similarity among those sparse vectors simultaneously. This constraint guarantees to reconstruct the HSI precisely even with a quite low sample rate and can select the necessary endmembers from the endmember library automatically for unmixing. LSSHUCS is further extended to a more general one, which tactfully settles the spectrum variation problem, and an augmented Lagrangian algorithm is elaborated meticulously to solve the inverse linear problem in LSSHUCS. Extensive experimental results on both synthetic and real hyperspectral data demonstrate that the proposed method surpasses several state-of-the-art methods on reconstruction accuracy.

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. In this paper, an efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and Tucker Decomposition (DWT-TD), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and TD to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance of the proposed method. Moreover, we show the impact of compression HSIs on the supervised classification and linear unmixing.

The data size of hyperspectral image is too large for storage and transmission, and it has become a bottleneck restricting its applications. So it is necessary to study a high efficiency compression method for hyperspectral image. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. Fractal coding has the advantages of high compression ratio, resolution independence, and a fast decoding speed, but its application in the hyperspectral image compression field is not popular. In this paper, we propose a novel algorithm for hyperspectral image compression based on hybrid prediction and fractal. Intraband prediction is implemented to the first band and all the remaining bands are encoded by modified fractal coding algorithm. The proposed algorithm can effectively exploit the spectral correlation in hyperspectral image, since each range block is approximated by the domain block in the adjacent band, which is of the same size as the range block. Experimental results indicate that the proposed algorithm provides very promising performance at low bitrate. Compared to other algorithms, the encoding complexity is lower, the decoding quality has a great enhancement, and the PSNR can be increased by about 5 dB to 10 dB.

The compression of hyperspectral images becomes recently very attractive issue for remote sensing applications because of the volumetric data. In this paper, an efficient method for hyperspectral image compression is presented based on Tucker Decomposition (TD) and Discrete Wavelet Transform (DWT). The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of hyperspectral images. Our method not only exploits redundancies between bands but also uses spatial correlation of every image band. Simulation results applied on the real hyperspectral images show a remarkable compression ratio and quality.

Depending on the mammoth size of the hyperspectral image, the wavelet transform-based compression algorithm has achieved impressive performance in the compression of hyperspectral images. Set partition wavelet transform hyperspectral image compression algorithms have superior performance than other transform compression algorithms, such as embeddedness, low coding complexity and high coding efficiency. Fractional wavelet-based zero memory set partitioned embedded block (ZM-SPECK) reduces the demand for transform memory and coding memory with at par transform complexity and high coding efficiency. But comparing every coefficient/block/set for each frequency frame with the current threshold is time-consuming. The present algorithm deals with the complexity and memory of the transform image coding algorithms. The block-based fractional wavelet filter delivers exact transform results like other wavelet transforms, but demands the least transform memory with at par wavelet transform complexity. With the employment of the low complexity zero memory set partitioned embedded block (LC-ZM-SPECK), the coding complexity of the compression algorithm is further reduced. The simulation results show that the proposed compression algorithm reduces the overall complexity by ~ 25% to other state-of-the-art compression algorithms and reduces the transform memory by ~ 40%, making it a suitable choice for the resource-constrained hyperspectral image sensors.

In order to improve the detection and recognition ability of multiscale hyperspectral images with weak edges, a lossless compression algorithm of multiscale hyperspectral images with weak edges based on improved deep learning is proposed. Using deep-level feature information extraction method to extract fine-grained features of cancer cells from multiscale hyperspectral images with weak edges, constructing 3D surface structure reconstruction model of multiscale hyperspectral images with weak edges, combining with variational Bayesian filter detection method to decompose multiscale color structure features of multiscale hyperspectral images with weak edges, and constructing gradually refined output structure model of multiscale hyperspectral images with weak edges, Fuzzy image coarse-scale neural network learning method is used to realize block matching and edge detection of multi-scale hyperspectral images with weak edges. Ultra-fine-grained features of multi-scale hyperspectral images with weak edges are extracted. Deep learning algorithm is used to adaptively optimize the lossless compression process of multi-scale hyperspectral images with weak edges, and the optimal design of lossless compression algorithm for multi-scale hyperspectral images with weak edges is realized. The simulation results show that the adaptive convergence of lossless compression of multi-scale weak edge hyperspectral images with this method is good, the feature resolution is strong, and the hyperspectral feature detection and recognition ability is good.

A competitive exception learning algorithm (CELA) is proposed which derives exceptional patterns from a given set of data pairs. In the first step of the algorithm, the output space is fuzzily partitioned (using a competitive learning algorithm) and then characterized by a so-called unconditional cluster membership distribution. In the second step, the input space is fuzzily partitioned as well by again using competitive learning. After learning, the input clusters represent locations in the input space which correspond to the most exceptional patterns in the output space. These patterns are characterized by so-termed conditional cluster membership distributions. In the third step, a fuzzy rule base is set up describing the exceptions in linguistic terms. In addition, the complete fuzzy input-output map can be estimated using a mathematical function. Besides describing the various steps of the new algorithm, a theoretical framework is introduced using an information-theoretical point of view: all output data points are supposed to be generated by a non-stationary stochastic, fuzzy information source. A summary concerning the results of certain simulations completes the description of CELA. The article is concluded with a discussion and outlook.

There has recently been a strong interest towards low-complexity approaches for hyperspectral image compression, also driven by the standardization activities in this area and by the new hyperspectral missions that have been deployed. This chapter overviews the state-of-the-art of lossless and near-lossless compression of hyperspectral images, with a particular focus on approaches that comply with the requirements typical of real-world mission, in terms of low complexity and memory usage, error resilience and hardware friendliness. In particular, a very simple lossless compression algorithm is described, which is based on block-by-block prediction and adaptive Golomb coding, can exploit optimal band ordering, and can be extended to near-lossless compression. We also describe the results obtained with a hardware implementation of the algorithm. The compression performance of this algorithm is close to the state-of-the-art, and its low degree of complexity and memory usage, along with the possibility to compress data in parallel, make it a very good candidate for onboard hyperspectral image compression.

The spectral analysis capabilities of Hyperspectral imaging produce large datasets that create problems when storing and transmitting the data, along with processing it in real-time. Traditional compression approaches that use transform-based and deep learning methods either need versatile adaptation features or considerable computational power. A real-time adaptive rate control hyperspectral image compression system based on FPGA and software co-design features the proposal. The proposed method optimizes compression efficiency through three elements, which include adaptive quantization and entropy coding and a dynamic rate control system. The method processes hyperspectral data at speeds faster than 25 frames per second while using less than 5 watts of power to deliver compression ratios between 10:1 and 50:1 and PSNR values from 35-45 dB, along with SSIM measures between 0.92 and 0.98. The approach achieves a lower bit rate level of 30–50% when compared to earlier research methods while delivering superior visual results. The proposed solution delivers an effective power-saving method for real-time hyperspectral image compression, which benefits satellite and UAV applications.

This study explores the utilization of Binary Embedded Zero Tree Wavelet Algorithms (BEZW) to compress hyperspectral images. The primary goal is to enhance the representation of spectral data while minimizing storage and transmission requirements. The BEZW algorithm employs wavelet transformations to leverage both spectral and spatial redundancies found in hyperspectral data cubes. Its embedded zero tree structure ensures efficient encoding of small coefficients with minimal overhead. The study evaluates the performance of the BEZW method utilizing various hyper-spectral datasets and compression settings, comparing it to other compression techniques. The results illustrate that the BEZW algorithm offers a promising approach to hyperspectral image compression, achieving competitive compression ratios while preserving spectral accuracy. This makes it a valuable option for applications where efficient hyperspectral data storage and transmission are crucial. The research contributes to the field of hyperspectral imaging by introducing an effective compression method that enhances data accessibility, simplifying the utilization of hyperspectral data in resource-constrained environments.

Compressed hyperspectral image reconstruction plays an important role in hyperspectral image processing, which aims to reduce the cost of transmission and recover the hyperspectral image from a small amount of data. Compressed sensing based methods have provided a new way for hyperspectral image reconstruction; however, the reconstruction problem is an ill-posed linear inverse problem, which needs some prior knowledge as constraint conditions. To make the most use of the prior knowledge, we introduce a block low-rank and three-dimension weighted total variation reconstruction algorithm. First, considering hyperspectral images have a piecewise smooth structure in both spatial and spectral domain, we use the total variation constraint in all three dimensions, and weight is utilized to trade off the contribution of the total variation in the different dimensions. Second, low-rank regularization is adopted in the reconstruction algorithm. To overcome the problem that the spectrum curves of different materials become similar which caused by the low-rank regular term, we divide the hyperspectral image into some non-overlapping blocks in the spatial domain and use low-rank regularization individually. Finally, taking into account the phenomenon that the rank of a block which has many elements in a given scene is usually higher than a normal block, we use one percent pixels in the original data to decide whether to perform low-rank constraint on the block or not. We use the fast iterative shrinkage/thresholding algorithm to solve the reconstruction problem. Extensive experiments demonstrate the superiority of the proposed model compared to other state-of-the-art reconstruction algorithms.

Sparse representation provides an efficient way for the compression of hyperspectral images in the literature. In this work, an improved version of the Spectral-Spatial Adaptive Sparse Representation (SSASR), Modified SSASR (MSSASR), is proposed for hyperspectral image compression. In the first step of the proposed method, superpixel maps are generated for adaptive spatio-spectral representation. Then, the best possible dictionary is computed for the representation of the data. Afterwards, sparse coefficients are determined depending on the dictionary by Simultaneous Orthogonal Matching Pursuit (SOMP) method. In the final step, the dictionary and sparse coefficients are encoded by quantization and entropy encoding. This paper has the following novelties: modified dictionary learning step, new ordering scheme and Differential Pulse Code Modulation (DPCM) usage. Owing to modified dictionary learning, the sparse coefficients can be represented more compact than traditional SSASR. By using of new ordering scheme, it is not needed to send the superpixel map as side information. Moreover, DCPM usage lowers the magnitudes of sparse coefficients. Thanks to these modifications, the proposed method achieves an important improvement on compression performance. In the experimental results, the proposed method is compared with PCA+JPEG2000, DWT+JPEG2000, 3D-SPECK, 3D-TARP and SSASR methods on Indian Pines, Washington DC Mall, Jasper Ridge and Moffett Field scenes. The evaluation is carried out not only using distance and similarity metrics, namely, signal-to-noise ratio, mean spectral angle and mean spectral correlation metrics but also computation times. Additionally, reconstruction quality in anomaly regions is also used for comparison. Experimental results show that the proposed method outperforms the other compression methods in terms of quality metrics and anomaly preserving performance.