Full text: Mapping without the sun

the condition probability p^X/w^ be called as 
multi-variable probability density function, and its expression 
P&l w k) = n .„J, c , /2 expf|(X-prfSÎiX-ti)] 
(2ft) I *->/t I ^ 
1 n k 
"k H 
s k =- L tZk x j -Mk)( x j -Mk) T ] 
n k 1 7=1 
In this formula, m is the band number; ¡Ll k is the mean value 
vector of training samples; S k is the covariance matrix; Yl k 
is the pixel number in category W k . 
3.1 Detection Principle 
Basic principle: At First this paper carries the MLC on the sole 
period RS image, and obtains the classification results. Then 
makes the pretreatment of space transformation separately 
between the present land use map and the classified RS image, 
and makes the two have the very good geometry space 
uniformity. Then overlays the present land use map and RS 
image in space, makes the land codes be related to spots, and 
ascertains pixels contained in each spot by Vertical Line Law. 
According to the Maximum Likelihood Classification results, 
this paper carries out the category attribute judgment in the 
land use spot unit utilizing the Spot Class Statistic and Judge 
Function, and makes the attribute matching between the 
classified and judged spot units and the object codes, then 
detects the changed spots, marks them in high-light, and 
calculates those areas. 
3.2 Detection Flow and Key Technique 
Detection procedure has two key questions to need to solve in 
practical realization, which can be divided explaining as 
(1) Determining pixel sets contained in each plot 
There are several methods to examine whether the given point 
locates in the given region or not, such as the Radial Law, the 
Comer Law, the Area Law, and the Vertical Line Law. This 
paper uses the Vertical Line Law to processing, and its 
principle is: 
Obtains all pixel sets in the irregular spot. It can be solved by 
judging whether the point is in the polygon or not. 
The practical algorithm is the Vertical Line Law: makes the 
perpendicular line to under from point needed to judge, and 
calculates the number of intersection points between the 
perpendicular line and the spot. If the number is odd, this point 
will be in the spot; otherwise, this point will be outside the 
In order to enhance the operating efficiency of program, firstly 
obtains the all pixels involved in the smallest outside rectangle 
of one polygon, which will reduce the searching scope. 
Secondly do the judgment using the Vertical Line Law. 
Before to calculate intersection points, it should filtrate 
voluminous segments through judging whether the each 
segment of spot contains the given pixel point in X axis, 
because only the segments having given pixel point have the 
possibility to intersect with perpendicular line. By this way, it 
can enhance the operating efficiency of program. 
(2) Judge the category attribute of spot 
Considering there are various objects or mixed pixels involved 
in spots, it cannot obtain a unique classified object utilizing the 
Maximum Likelihood Classification. Therefore, the attribute 
judgment of spot cannot simply depend on one kind of object 
classification result. According to this problem, this paper 
proposes the Spot Class Statistic and Judge Function, it means 
that calculates the homogeneous category pixels in the same 
spot using statistical methods, and divides this spot to the 
category having the most pixels. 
Fig. I the flow chat of land use changing detection program 
4.1 Experiment Results 
In the classification experiments, the Maximum Likelihood 
Classification is used to carry on the classification based on the 
spectral feature, and obtains the classification results. Fig 2 is 
the map overlaying the MLC results and the present land use 
map. In this classified result, yellow expresses the vegetable 
plot, the aqua expresses the residential area, the bottle green 
expresses the paddy field, the purple expresses the dry 
farmland, and the black expresses water surface. This paper 
randomly selects 500 samples in the sample region covered by 
RS image, and constructs the corresponding confusion matrix 
to assess the accuracy of classification results. Table 1 is the 
confusion matrix of MLC results. And the calculated total

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