International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
TM Band-5 | TM Band-4 | TM Band-3 | SPOT
Entropy (bits) 6.48 6.01 5.00 6.17
Information amount (bits) 4.38 3.40 1.60 3.44
; Average code length 6.51 6.04 3.97 6.20
Huffnan Coding, | conpressionate 1.23 1.32 2.01 1.29
Uni-directional Average code length 5,22 4.85 3.07 4.50
differential coding Compression rate 1.53 1.65 2.01 1.78
Bidirectional Average code length 5.12 4.77 3.88 3.66
differential coding Compression rate 1.56 1.68 2.06 2.19
Table 2. Comparisons between Huffman coding and differential coding
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Figure 3. Grayscale histogram of the initial image
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Figure 4. Grayscale histogram of the differential image
3.2 District Forecast Differential Coding
Four types of representative land covers are selected and shown
in Figure 5. All of them are SPOT-5 panchromatic images with
a 2.5 meter spatial resolution, consisting of 1024 by 1024 pixels.
Differential encoding algorithm was applied to these four
images, with results shown in Table 3. The results accord with
what is expected, i.e., the compression rates of the images are in
reverse proportion to the information amounts. The land covers
with larger information amounts, e.g., urban areas and farm
lands, would achieve smaller compression rates, and vice versa
(e.g., mountain areas and water bodies). Therefore, according to
the negative correlation between information amount and
compression rate, a district forecast differential encoding
method is proposed to further improve the compression rates of
remote sensing images.
First, images may be categoried by the texture complexities,
e.g., built-up areas, farmlands, forests, deserts, water, etc. For
different land cover types, differential encoding algorithm is
applied. And the more effective one between unidirectional or
bidirectional differential encoding is selected for each land
cover type according to the compression efficiencies. At last,
the corresponding compression modules would be built for each
type.
Moreover, due to the definitive orbits of satellites, land cover
types inherent to remote sensing images are relatively certain.
As is mentioned above, the information amounts inherent to
images are different because of different land cover types,
which results in different compression rates. Hence, historic
remote sensing images may be firstly collected to judge the land
cover types when satellite flying over certain districts. In other
words, the orbit is divided into districts according to land cover
types. As the satellite flying over oceans, the differential
encoding module for this type may be executed to compress the
image data. Similarly, the differential encoding module for
forest may be called when the satellite flying over a forested
area. In this way, the differential coding algorithms may be
applied to images according to different districts so as to obtain
higher compression rates and sufficiently and effectively usage
of the limited storage space on-board.
3.3 Near Lossless Compression
To testify, the operator is applied to the SPOT-5 image in
Figure 2(b) one time, two times and three times, respectively.
The result images are shown in Figure 6, and the corresponding
compression results of Huffman coding and differential
encoding are listed in Table 4, where the times of edge
protection smoothing set to naught equals to directly applying
the encoding algorithms to the original image.
According to Table 4, with the times of edge protection
smoothing increasing, the compression rates of Huffman coding
are seldom improved. However, significant improvements in
both the average code length and compression rate may be
achieved after the application of differential coding. Moreover,
considering different uncertainties of different land cover types,
the same comparative experiments are applied to the four
images shown in Figure 5, the results of which are listed in
Table 5. It is obvious that different land cover types will obtain
different compression rates. Furthermore, the lower the
information amount is, the higher the compression rates would
be achieved.
