Lossless Predictive Compression of Hyperspectral Images

Wang, Hongqiang; Sayood, Khalid

doi:10.1007/0-387-28600-4_2

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Spatial predictive methods have been upgraded to perform inter-band compression via increasing the size of the neighbourhood from 2D to 3D. However, according to Wang and Sayood (2006), the direct extension from 2D to 3D may not always provide tangible benefits, and sometimes can prove to be detrimental. Therefore, it is necessary to develop predictors that are specialized for 3D hyperspectral images.…”

Section: Hyperspectral Image Compression: An Introductionmentioning

confidence: 99%

Investigation into lossless hyperspectral image compression for satellite remote sensing

Noor

Vladimirova

2013

International Journal of Remote Sensing

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Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-infrared (IR), mid-IR and thermal IR portions of the spectrum, thus requiring large data storage on board the satellite and high bandwidth of the downlink transmission channel to ground stations. Image compression techniques are required to compensate for the limitations in terms of on-board storage and communication link bandwidth. In most remote-sensing applications, preservation of the original information is important and urges studies on lossless compression techniques for on-board implementation. This article first reviews hyperspectral spaceborne missions and compression techniques for hyperspectral images used on board satellites. The rest of the article investigates the suitability of the integer Karhunen-Loève transform (KLT) for lossless inter-band compression in spaceborne hyperspectral imaging payloads. Clustering and tiling strategies are employed to reduce the computational complexity of the algorithm. The integer KLT performance is evaluated through a comprehensive numerical experimentation using four airborne and four spaceborne hyperspectral datasets. In addition, an implementation of the integer KLT algorithm is ported to an embedded platform including a digital signal processor (DSP). The DSP performance results are reported and compared with the desktop implementation. The effects of clustering and tiling techniques on the compression ratio and latency are assessed for both desktop and the DSP implementation.

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Section: Hyperspectral Image Compression: An Introductionmentioning

confidence: 99%

Investigation into lossless hyperspectral image compression for satellite remote sensing

Noor

Vladimirova

2013

International Journal of Remote Sensing

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“…Some types of sensors can generate more than 1 TB of data in one day. Hence the use of a robust data compression techniques has become very important for archiving and transferring purposes 1,2 . Because of the importance of generating highly accurate information about the atmosphere, clouds, and surface parameters provided by the RS sensors, lossy compression techniques are not desirable 3 .…”

Section: Introductionmentioning

confidence: 99%

A new method for compression of remote sensing images based on an enhanced differential pulse code modulation transformation

Ghamisi¹,

Sepehrband²,

Kumar³

et al. 2013

ScienceAsia

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Remote sensing sensors generate useful information about climate and the Earth's surface, and are widely used in resource management, agriculture, and environmental monitoring. Compression of the RS data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as from remote sensing. In this paper, a less complex and efficient lossless compression method for images is introduced. It is based on improving the energy compaction ability of prediction models. The proposed method is applied to image processing, RS grey scale images, LiDAR rasterized data, and hyperspectral images. All the results are evaluated and compared with different lossless JPEG and a lossless version of JPEG2000, thus confirming that the proposed lossless compression method leads to a high speed transmission system because of a good compression ratio and simplicity.

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“…Because of the large storage volume and spectral and spatial sample data redundancy [2], it is economical to compre ss hyperspectral images before transmitting them. Basic compression methods for hyperspectral imagery are transform coding based algorithms [3]- [5], Vector Quantization (VQ) based algorithms [6,7], Differential Pulse Code Modulation (DPCM) algorithm [8], and Adaptive Differential Pulse Code Modulation (AD-PCM) [9]. Conventionally, image decompression must be applied after compression.…”

Section: Introductionmentioning

confidence: 99%

A novel scheme for the compression and classification of hyperspectral images

Xie

Bose

Merényi

2009

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

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Since hyperspectral images are very large, it is desirable to compress them before transmission. After receiving the compressed image, decompression is applied before performing image classification and other operations. In this paper, a new processing scheme is proposed, where image transform and quantization are applied for image compression at the transmitter and classification is performed directly on the compressed data at the receiver. The advantage of this scheme is that fewer computation s are needed. Computer simulations are performed on hyperspectral imagery.

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Lossless Predictive Compression of Hyperspectral Images

Cited by 8 publications

References 25 publications

Investigation into lossless hyperspectral image compression for satellite remote sensing

Investigation into lossless hyperspectral image compression for satellite remote sensing

A new method for compression of remote sensing images based on an enhanced differential pulse code modulation transformation

A novel scheme for the compression and classification of hyperspectral images

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