空间光学成像系统图像压缩全面指南

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本文是一篇关于光学空间成像系统图像压缩的教程，由《地球与遥感科学杂志》于2014年9月发布。随着公共政策和私营部门对太空探索的兴趣日益增长，卫星和航空器上的传感器技术不断进步，所获取的遥感数据量大幅度增加。为了支持如此庞大的数据传输，并保持图像细节的完整性，高效的数据压缩技术变得至关重要。文章首先概述了空间数据系统咨询委员会最新批准的一些遥感数据压缩标准，这些标准包括JPEG 2000、JPEG-X和JPEG-LS等。同时，文章也探讨了ISO/IEC图像编码标准的应用，尽管这些标准可能在某些情况下更为通用。压缩方法被分为三个主要类别：单波段和多波段压缩，以及无损、有损和近无损压缩。在技术层面，文章深入剖析了利用光谱特性进行压缩的方法。这包括基于预测的压缩技术，通过分析相邻像素之间的光谱相似性来减少数据冗余；以及基于变换的压缩，如离散余弦变换（DCT）或小波变换，将图像分解为不同频率成分，然后丢弃或量化高频成分以减小存储需求。此外，文中还展示了AVIRIS和Hyperion图像的实际压缩效果，这两种传感器分别提供高分辨率的窄带和宽谱数据，展示如何在保持图像质量的同时，有效地降低数据传输的需求。总结来说，这篇论文为光学空间成像系统中的图像压缩提供了全面的指南，帮助技术人员选择合适的压缩算法，优化数据传输效率，满足日益增长的太空观测需求。对于那些处理和分析遥感数据的科研人员、工程师以及政策制定者来说，理解和应用这些压缩技术是必不可少的。

SEPTEMBER 2014 ieee Geoscience and remote sensinG maGazine

and hyperspectral images. In Section IV we take a more

general approach, and discuss a set of possible modifica-

tions to the CCSDS-123 standard, including near-lossless,

lossy compression and rate control. Unlike Section II and

III, which aim at describing the main steps of the existing

standards in a tutorial way, Section IV has a more general

scope and reviews more about the underlying theory, high-

lighting open research issues and possible solutions, as

well as other possible approaches. It has to be mentioned

that, while this paper describes extensions of CCSDS-123,

other options are also being considered, such as a three-

dimensional extension of CCSDS-122.

D. DAtAset

Throughout the paper we will provide compression results

aimed at assessing the performance of the techniques

presented in the paper. To this end, a set of hyperspectral

images will be employed, as shown in Table 1. These are

AVIRIS

1,2

and Hyperion

2,3

hyperspectral images, publicly

available for download. Technical names and sizes are pro-

vided in Table 1, along with the zero-order entropy of the

images. All images are 16 bits per pixel per band (bpppb),

except for Hawaii and Maine that are 12 bpppb. Uncali-

brated images are stored as unsigned integers, whereas

calibrated images are stored as signed integers. Considered

calibrated images are radiance images.

For both AVIRIS and Hyperion images, the multi-com-

ponent volume can be used to produce a spectral signature

for each of the spatial pixels. These spectral signatures are

then employed in remote sensing analysis as classification

or anomaly detection. Fig. 2 illustrates one AVIRIS image

and a spectral signature for some pixels.

II. LOSSLeSS DATA COMPReSSION

As pointed out in Section I, we summarize here the most

recent standard for remote sensing data compression. The

CCSDS created the Multispectral Hyperspectral Data

http://avirir.jpl.nasa.gov

http://compression.jpl.nasa.gov/hyperspectral/

http://earthexplorer.usgs.gov

Compression (MHDC) Working Group in June 2007 to

issue a Recommended Standard for Multi- and Hyper-

spectral image compression. The motivation for creating

this Working Group is explained in this excerpt from [8]:

On-board data compression is needed to make full use of lim-

ited spacecraft resources like data storage and downlink capacity.

Multispectral & hyperspectral images can occupy enormous data

volumes, and so compression algorithms specifically designed to

exploit the three-dimensional structure of such images can pro-

vide tremendous benefit to space missions.

Thanks to the work of several member space agencies,

observer space agencies and committed participants, the

Working Group was able to deliver CCSDS-123 standard

[9] in May 2012. The standard is intended for on-board

lossless compression of multi- and hyperspectral images.

The coding technique is built upon Fast Lossless (FL) com-

pression algorithm [10] and is able to provide state-of-the-

art coding performance for a large collection of remote

sensing sensors. As it is oriented towards on-board opera-

tion, i.e., in resource-constrained scenarios, the design

was carefully conceived to require a very low computa-

tional complexity.

FIgURe 2. AVIRIS volume and spectral signature at various locations.

Water

Vegetation

Soil

Wate

Soi

TaBlE 1. aVIRIS and HyPERIon IMagES uSEd In THE ExPERIMEnTS. TEcHnIcal naMES,

SIzES and zERo-oRdER EnTRoPIES (In BPPPB) aRE PRoVIdEd.

SEnSoR naME TEcHnIcal naME

SIzE xyz##

EnTRoPy

AVIRIS Hawaii Uncalibrated f011020t01p03r05 614 # 512 # 224 9.09

Maine Uncalibrated f030828t01p00r05 680 # 512 # 224 8.55

Yellowstone Radiance Sc0 f060925t01p00r12 677 # 512 # 224 10.33

Yellowstone Uncalibrated Sc0 f060925t01p00r12 680 # 512 # 224 12.62

Hyperion Agricultural Calibrated EO1H0280342004074110PX 256 # 3129 # 242 10.05

Coral Reef Calibrated EO1H0830742003120110PW 256 # 3127 # 242 8.46

Urban Calibrated EO1H0440342002212110PY 256 # 3176 # 242 10.01

Erta Ale Uncalibrated EO1H1680502010057110KF 256 # 3242 # 242 9.46

Lake Monona Uncalibrated EO1H0240302009166110PF 256 # 3352 # 242 9.91

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