Normalized difference vegetation index calculations from JPEG2000-compressed Landsat 7 images
Normalized difference vegetation index calculations from JPEG2000-compressed Landsat 7 images
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根据 JPEG2000 压缩的 Landsat 7 图像计算归一化植被指数差异
DOI：
10.1117/12.557954
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
E. Dereniak
中科院分区：
文献类型：
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作者：
J. Scholl;K. Thome;E. Dereniak
遥感领域的一个持续问题是图像通常会消耗大量的内存和传输带宽，从而限制了获取的数据量。使用高质量的图像压缩算法，如基于小波的JPEG 2000，已被提出来减少大部分的内存和带宽开销，但是，这些压缩算法往往是有损的，遥感界一直小心翼翼地实施这样的算法，担心退化的数据。我们探讨这个问题的JPEG 2000压缩算法应用于Landsat-7增强型专题制图仪（ETM+）图像。这项工作研究的影响，有损压缩可以有归一化差异植被指数（NDVI）的检索。我们已经计算出的NDVI从JPEG 2000压缩的红色和近红外Landsat-7 ETM+图像，并与未压缩的值在每个像素进行比较。此外，我们还研究了压缩对NDVI产品本身的影响。我们发现，无论是空间分布的归一化植被指数和整体的归一化植被指数的像素统计在图像中的变化很小后，图像已被压缩，然后在很宽的比特率范围内重建。
An ongoing problem in remote sensing is that imagery generally consumes considerable amounts of memory and transmittance bandwidth, thus limiting the amount of data acquired. The use of high quality image compression algorithms, such as the wavelet-based JPEG2000, has been proposed to reduce much of the memory and bandwidth overhead; however, these compression algorithms are often lossy and the remote sensing community has been wary to implement such algorithms for fear of degradation of the data. We explore this issue for the JPEG2000 compression algorithm applied to Landsat-7 Enhanced Thematic Mapper (ETM+) imagery. The work examines the effect that lossy compression can have on the retrieval of the normalized difference vegetation index (NDVI). We have computed the NDVI from JPEG2000 compressed red and NIR Landsat-7 ETM+ images and compared with the uncompressed values at each pixel. In addition, we examine the effects of compression on the NDVI product itself. We show that both the spatial distribution of NDVI and the overall NDVI pixel statistics in the image change very little after the images have been compressed then reconstructed over a wide range of bitrates.