The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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3.3 TM and SAR Image Registration 
Image matching error is impacted with results on land-use 
change monitoring, to get reliable analysis results of the images 
need high match accuracy. As a matching error of the element 
space will result in 50 percent of the false changes. To control 
the error rate of 10 percent, matching error on the image must 
be less than 0.2 pixel, or more like small, the prerequisite and 
basis of remote sensing image fusion is the registration for 
multi-source remote sensing images, the registration error 
requirements less than one pixel. TM and SAR image resolution 
were 30 meters and 12.5 meters, a greater difference, and the 
SAR images have noise dot, in order to improve the accuracy of 
registration, uniform selection for homonymy points, as clearly 
building, rivers changes smaller, texture is clear such as roads 
for the registration, a total of 26, using a polynomial correction, 
the error in control as 0.3 pixel. It was satisfied for the 
registration accuracy requirements in the test. 
4. TM AND SAR IMAGE FUSION 
TM images received surface features information by the 
electromagnetic spectrum stretching from 0 to 255 between the 
Gray values; SAR images recorded form 0 to 65535 between 
the integer values in the microwave band features the return 
value, different Gray Represents the relative characteristics of 
different features. The homology sensors, can direct use of 
video Gray value integration and subsequent processing, and 
sensors for different sources, because of its imaging, spectral 
range and the characterization of surface features and other 
characteristics are different, then different images Gray value 
was not comparable, pixel Gray value for fusion will bring 
about the corresponding error. Sensor characteristics need to, 
through the use of physical corresponding model and the 
reflectivity of the true features and value after the scattering, 
accordingly for images fusion and information extracted. 
The SAR and TM images using the fusion algorithm integration 
of wavelet and HIS, its integration with the basic approach: the 
largest amount of information on the composition of the TM5, 
TM4, TM3 for IHS transform, I get the strength component 
wavelet transform, using the wavelet function for Sym4 wavelet. 
SAR image and the image / used Sym4 wavelet for a wavelet 
decomposition, with the approximate SAR images AS replace 
the approximate / image AI, to impose anti-wavelet transform 
to generate 1' image and then IHS inverse transform, and you 
will get a new fusion images of R\ G\ B' (Figure 4). 
Figure 4. Fusion of TM and SAR images 
5. MINE TYPICAL FEATURE EXTRACTION 
According to the fusion of SAR and TM images, classified the 
mining area features. In order to enhance the classification 
results credibility of features, using remote sensing feature of 
the land cover information from the tiered approach that, 
according to the different and various targets Characteristics, 
take the appropriate information extraction methods, 
respectively, the establishment of thematic information layer, 
and then merger the thematic information layer to the overall 
classification, hierarchical extraction methods to take fully into 
account the different characteristics of typical features, the 
results of the classification can be making area measurement 
accuracy estimates, such as change detection analysis, and 
generate thematic maps throughout the category. 
According to the land surface monitoring purposes, the study 
area is divided into hydro-geological environment (include 
natural water and geological conditions), agricultural land 
(include grassland, paddy fields, dry land, others) and 
vegetation growth, land use and land cover (include traffic, 
residents, Mining land, forest land) and soil conditions as 
several major categories. The special features which not 
recognized by the fusion images, to further explore the solution 
method of microwave remote sensing and interference SAR 
images. 
(1) Water extraction (canals, reservoirs, ponds and stagnant 
water of the collapse) 
Waters of the study area include canals, reservoirs, ponds and 
stagnant water of the collapse, can exclude the water impact 
from the mountain shadows and the shadow of buildings. On 
the basis of the results from water extraction, conducted 
extraction of canals, reservoirs, ponds, according to the 
differences characteristics in space, such as canals were bending 
the long strips; reservoir area are relatively large, border is 
relatively smooth; ponds shape are sleek, oval or rules similar 
to the quadrilateral. Because microwave remote sensing 
particularly sensitive to water, on the radar vegetation canopy 
soil or snow cover has some penetration, we can detect these 
objects on the surface and sub-surface information, so it can use 
the SAR images for the water body Monitoring. 
(2) Building land extraction (urban land, rural settlements, an 
independent mining site, transportation sites) 
Towns, settlements, land transport and mining sites are easy to 
distinguish from the fusion images, the merits to retain the 
spectral characteristics of TM images, but also retained texture 
information of the high-resolution SAR images. 
(3) Green space and farmland extraction (paddy field, dry land) 
The study areas include green woodland and grassland, 
farmland including rice fields and mainly dry. In the TM 
images similar spectrum information, information also is not 
very clear after the fusion, distinction between the regional 
crops can use SAR images to distinguish between them by 
scattering differences, related to integrated data, will distinguish 
types of crops. 
(4) Mining subsidence land extraction 
As Mining long time and scope, ground subsidence is quite 
serious, especially in the rainy season there are stagnant water