Lossless to Lossy Dual-Tree BEZW Compression for Hyperspectral Images

Cheng, Kai-Jen; Dill, J.C.

doi:10.1109/tgrs.2013.2292366

Cited by 34 publications

(7 citation statements)

References 21 publications

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“…4, t Φ ↓ j is the instant in which packets Φ ↑ k , 0 ≤ k ≤ j have been transmitted plus the time needed to compute Φ ↓ j . This quantity is expressed by the left argument of the max{•} operator in (5). For the other packets of the figure, the earliest instant is when the previous packet (i.e., Φ ↓ j−1 ) has Fig.…”

Section: Synchronization Aspectsmentioning

confidence: 99%

“…Once the data are compressed -either with or without loss-to a size that fits the bit budget of the downlink, they are (stored and then) transmitted during the time in which the ground station(s) is in contact with the satellite. Among others, standards and coding systems devised for the coding of satellite images using lossless and lossy regimes are respectively described in [1]- [6] and [2], [5], [7], [8].…”

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confidence: 99%

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Dual Link Image Coding for Earth Observation Satellites

Aulí-Llinàs

Marcellin

Sánchez

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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The conventional strategy to download images captured by satellites is to compress the data on board and then transmit them via the downlink. It often happens that the capacity of the downlink is too small to accommodate all the acquired data, so the images are trimmed and/or transmitted through lossy regimes. This paper introduces a coding system that increases the amount and quality of the downloaded imaging data. The main insight of this work is to use both the uplink and the downlink to code the images. The uplink is employed to send reference information to the satellite so that the on-board coding system can achieve higher efficiency. This reference information is computed on the ground, possibly employing extensive data and computational resources. The proposed system is called dual link image coding. As it is devised in this work, it is suitable for Earth observation satellites with polar orbits. Experimental results obtained for datasets acquired by the Landsat 8 satellite indicate significant coding gains with respect to conventional methods.Index Terms-Dual link image coding, satellite data transmission, JPEG2000. I. INTRODUCTIONS INCE the launch of Sputnik 1 in 1957, the number and diversity of satellites orbiting the Earth has not stopped growing. Nowadays, there are more than 2,000 satellites dedicated to communications, navigation, astronomy, weather, reconnaissance, and Earth observation (EO). These satellites provide services in our every-day lives such as TV, Internet, weather forecast, or geolocation, among others.Most satellites are situated in either a geostationary or a polar orbit (see Fig. 1). Satellites in geostationary orbits are at an altitude of approximately 22,000 miles and are located above a stationary point on the Earth's equator. Their communications rely on ground stations that have an uninterrupted line of sight to the satellite. Despite the fact that the high altitude restricts the communication to medium/low capacity channels, these satellites are in constant contact with the ground station, so they can transmit great amounts of data. They are commonly employed for communication-related tasks.

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Section: Synchronization Aspectsmentioning

confidence: 99%

mentioning

confidence: 99%

Dual Link Image Coding for Earth Observation Satellites

Aulí-Llinàs

Marcellin

Sánchez

et al. 2018

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For packets Φ For the other packets of the figure, the earliest instant is when the previous packet (i.e., Φ ↓ j−1 ) has already been transmitted, which is expressed in the right part of the max(·) expression. Note that (5) assumes that processing times c j are shorter than transmission times T Φ ↑ j . The synchronization needed between the ground station and the satellite reduces the effective bit budget of both channels since, as seen in Fig.…”

Section: Synchronization Aspectsmentioning

confidence: 99%

“…Once the data are compressed -either with or without loss-to a size that fits the bit budget of the downlink, it is (stored and) transmitted during the time in which the ground station(s) are in contact with the satellite. Among others, standards and coding systems devised for the coding of satellite images using lossless and lossy regimes are respectively described in [1]- [6] and [2], [5], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Coding Scheme for the Transmission of Satellite Imagery

Aulí-Llinàs

Marcellin

Sánchez

et al. 2016

2016 Data Compression Conference (DCC)

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The coding and transmission of the massive datasets captured by Earth Observation (EO) satellites is a critical issue in current missions. The conventional approach is to use compression on board the satellite to reduce the size of the captured images. This strategy exploits spatial and/or spectral redundancy to achieve compression. Another type of redundancy found in such data is the temporal redundancy between images of the same area that are captured at different instants of time. This type of redundancy is commonly not exploited because the required data and computing power are not available on board the satellite. This paper introduces a coding scheme for EO satellites able to exploit this redundancy. Contrary to traditional approaches, the proposed scheme employs both the downlink and the uplink of the satellite. Its main insight is to compute and code the temporal redundancy on the ground and transmit it to the satellite via the uplink. The satellite then uses this information to compress more efficiently the captured image. Experimental results for Landsat 8 images indicate that the proposed dual link image coding scheme can achieve higher coding performance than traditional systems for both lossless and lossy regimes.

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“…A low complexity KLT-based algorithm in Blanes and Serra-Sagristà [2010] was introduced, its performance is better than DWT when it is used with the JPEG coding. In Cheng and Dill [2014], a lossless to lossy compression scheme for hyperspectral images based on a dual-tree Binary Embedded Zerotree Wavelet (BEZW) algorithm has been presented. The impact of lossy compression on spectral unmixing, and supervised classification using Support Vector Machine (SVM) was investigated in García-Vílchez et al [2011].…”

Section: Introductionmentioning

confidence: 99%

Compression and noise reduction of hyperspectral images using non-negative tensor decomposition and compressed sensing

Hassanzadeh

Karami

2016

European Journal of Remote Sensing

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Hyperspectral images (HSI) are usually volumetric and require alot of space and time for archiving and transmitting. In this research, a new lossy compression method for HSI is introduced based on non-negative Tucker decomposition (NTD). This method consider HSI as a 3D dataset: two spatial dimensions and one spectral dimension. The NTD algorithm decomposes the original data into a smaller 3D dataset (core tensor) and three matrices. In the proposed method, the Block Coordinate Descent (BCD) method is used to find the optimal decomposition, which is initialized by using Compressed Sensing (CS). The obtained optimal core tensor and matrices are coded by applying arithmetic coding and finally the compressed dataset is transmitted. The proposed method is applied to the real dataset, simulation results show that in comparison with well-known lossy compression methods such as 3D SPECK and PCA+JPEG2000, the proposed method achieves the highest signal to noise ratio (SNR) at any desired compression ratio (CR) while noise reduction is simultaneously acquired.

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Lossless to Lossy Dual-Tree BEZW Compression for Hyperspectral Images

Cited by 34 publications

References 21 publications

Dual Link Image Coding for Earth Observation Satellites

Dual Link Image Coding for Earth Observation Satellites

Coding Scheme for the Transmission of Satellite Imagery

Compression and noise reduction of hyperspectral images using non-negative tensor decomposition and compressed sensing

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