CN104615999A - Landslide debris flow area detection method based on sparse representation classification - Google Patents

Abstract

The invention provides a landslide debris flow area detection method based on sparse representation classification. The method includes: dividing a target area image to be detected into multiple image blocks to be detected with the same sizes, analyzing and judging the brightness of three channels of red, green and blue through a sparse representation classification method, obtaining a suspected landslide debris flow image block collection, presetting the mean value of the difference between the first threshold value and the sparse representation errors; obtaining the landslide debris flow severely damaged area and the area boundaries in the suspected landslide debris flow image block collection through a connected area detection method; obtaining the land type of the image of each point before a disaster according to the position of each point of the area boundaries, obtaining the expansion degree influence coefficient of each point corresponds to the disaster loss weight according to the preset land type, obtaining the extended distance of the landslide debris flow area according to the mean value of the difference between the preset first threshold value and the sparse representation errors, and obtaining the landslide debris flow general damaged area through the extension of the landslide debris flow seriously damaged area. The landslide debris flow detection method is high in detection efficiency and accuracy and low in false alarm rate.

Description

Based on the landslide method for detecting area of rarefaction representation classification

Technical field

The present invention relates to landslide region detection technical field, particularly relate to a kind of landslide method for detecting area based on rarefaction representation classification (sparse representation-based classifier is called for short SRC).

Background technology

Define according to the theory of calamity, landslide to refer on slope that certain a part of ground is under gravity (comprising the using dynamic and static pressures of the gravity of ground own and underground water) effect, effect and phenomenon along certain weak structural face (band) generation shear displacemant integrally to movement below slope; And rubble flow refers in the dangerously steep area of mountain area or other cheuch deep gullies, landform, because heavy rain, severe snow or other disasteies landslide of causing carry the special mighty torrent of a large amount of silt and stone.The global feature such as profile, shape that landslide and the Forming Mechanism of rubble flow make its region both ununified on remote sensing images, the local features such as uniform texture, gradient are not had yet, existing detection method is mostly applicable to landslide and generates position and its periphery morphologic characteristics and have the situation comparatively obviously distinguished or assist by information such as DEM and carry out region, scope interpretation, exist and require harsh to Data Source and lack the problems such as broad applicability, be not suitable for utilizing xx data to carry out the application requirement of the condition of a disaster assessment.

Given this, how efficient, high accuracy is carried out to landslide region and the detection of low false alarm rate becomes the current technical issues that need to address.

Summary of the invention

For defect of the prior art, the invention provides a kind of landslide method for detecting area based on rarefaction representation classification, can carry out efficient detection to the landslide region of target area Aerial Images, accuracy is high, and false alarm rate is low.

First aspect, the invention provides a kind of landslide method for detecting area based on rarefaction representation classification, comprising:

Target area testing image is divided into the identical M of a size testing image block, M be greater than 1 integer;

The method utilizing rarefaction representation to classify is analyzed the three-channel brightness of described testing image block red, green, blue and differentiates, to obtain the set of the doubtful landslide image block in testing image block, and obtain the average of the difference of preset first threshold value and rarefaction representation error;

Utilize connected region detection method, in the set of described doubtful landslide image block, obtain landslide seriously damage region and zone boundary thereof;

According to the position of each point on described zone boundary, obtain the land type of each point before calamity in image;

According to the land type of described each point before calamity in image and disaster-stricken loss weight corresponding to default land type, obtain the influence coefficient of degree of expansion corresponding to each point;

According to the influence coefficient of the average of described preset first threshold value and the difference of rarefaction representation error and degree of expansion corresponding to each point, obtain the extended range in landslide region, and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region.

Alternatively, the described method utilizing rarefaction representation to classify is to the three-channel brightness analysis of described testing image block red, green, blue and differentiation, to obtain the set of the doubtful landslide image block in testing image block, and obtain the average of the difference of preset first threshold value and rarefaction representation error, comprising:

By testing image block carry out vectorization, normalization successively;

Complete dictionary A was built according to the typical sample of red, green, blue triple channel component ^q(q=R, G, B), wherein, R, G, B are respectively red, green, blue triple channel component;

Obtain the testing image block after vectorization and normalization rarefaction representation coefficient

According to described rarefaction representation coefficient to described testing image block be reconstructed, obtain rarefaction representation error

According to described rarefaction representation error obtain preset first threshold value with rarefaction representation error difference

Will compare with default Second Threshold, during>=default Second Threshold, determine testing image block red/green/blue channel component image block is doubtful landslide image block;

At testing image block red, green, blue triple channel component image block when being doubtful landslide image block, determine that this testing image block is the doubtful image block of landslide, and then obtain the set of doubtful landslide image block;

Described preset first threshold value when acquisition testing image block is landslide doubtful image block with rarefaction representation error difference average

Alternatively, the testing image block after described acquisition vectorization and normalization rarefaction representation coefficient comprise:

Testing image block after utilizing orthogonal matching pursuit OMP algorithm to obtain vectorization and normalization rarefaction representation coefficient

Alternatively, described utilize orthogonal matching pursuit algorithm OMP algorithm to obtain vectorization and normalization after testing image block rarefaction representation coefficient comprise:

According to the testing image block after vectorization and normalization with red, green, blue Three Channel Color component dictionary A ^q(q=R, G, B), establishing target function:

$\begin{array}{ccc}{\hat{x}}^q=\arg \min {\left|\right|x^q\left|\right|}_1& s.t.& A^q{x}^q=y_p^q(q=R,G,B;p=\mathrm{1,2},...,M);\end{array}$

Solve objective function, obtain the testing image block after vectorization and normalization rarefaction representation coefficient

Alternatively, described according to described rarefaction representation coefficient to described testing image block be reconstructed, obtain rarefaction representation error comprise:

According to described rarefaction representation coefficient by the first formula to described testing image block be reconstructed, obtain rarefaction representation error

Described first formula is:

$r_p^q\left(y_p^q\right)={\left|\right|y_p^q-A^q{\hat{x}}^q\left|\right|}_2(q=R,G,B;p=\mathrm{1,2},...,M).$

Alternatively, described general compare with default Second Threshold, also comprise:

? when < presets Second Threshold, determine testing image block red/green/blue channel component image block is non-landslide image block.

Alternatively, described default Second Threshold is 0.

Alternatively, describedly utilize connected region detection method, in the set of described doubtful landslide image block, obtain landslide seriously damage region and zone boundary thereof, comprising:

Utilize the connected region detection method of eight neighborhood method, in the set of described doubtful landslide image block, obtain landslide seriously damage region and zone boundary thereof.

Alternatively, the described connected region detection method utilizing eight neighborhood method, obtains landslide and seriously damages region and zone boundary thereof, comprising in the set of described doubtful landslide image block:

Using the central point of each doubtful landslide image block as a gauge point (i, j);

From each gauge point, each direction along eight neighborhood is searched for s time, if find other gauge points, then the party is upwards all designated as gauge point between original tally point and newfound gauge point;

Communication with detection is carried out to all gauge points obtained, in doubtful landslide image block, obtains landslide seriously damage region;

Wherein, the value of s depends on the size of gauge point position, target area image size and doubtful landslide image block.

Alternatively, the influence coefficient of the described average according to described preset first threshold value and the difference of rarefaction representation error and degree of expansion corresponding to each point, obtain the extended range in landslide region, and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region, comprising:

According to the influence coefficient of the average of described preset first threshold value and the difference of rarefaction representation error and degree of expansion corresponding to each point, extended range l (the i in landslide region is obtained by the 5th formula, j), and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region;

Wherein, described 5th formula is:

$l(i,j)={\stackrel{\&OverBar;}{\mathrm{\&lambda;}}}_p\&times;{\mathrm{\&theta;}}_n,$

for the average of the difference of preset first threshold value and rarefaction representation error, θ _nfor n-th kind of land type corresponding to each point on described zone boundary is to the influence coefficient of degree of expansion.

As shown from the above technical solution, the landslide method for detecting area based on rarefaction representation classification of the present invention, can carry out efficient detection to the landslide region of target area Aerial Images, accuracy is high, and false alarm rate is low.

Accompanying drawing explanation

The schematic flow sheet of the landslide method for detecting area based on rarefaction representation classification that Fig. 1 provides for one embodiment of the invention;

The schematic diagram of the connected region detection algorithm of the eight neighborhood method that Fig. 2 provides for the embodiment of the present invention;

Aerial Images after area, Zhouqu County Debris-flow Hazard that Fig. 3 provides for the embodiment of the present invention;

Fig. 4 analyzes for using the method utilizing rarefaction representation to classify in method described in the present embodiment of the present invention to carry out the three-channel brightness of red, green, blue to the Aerial Images block after the Debris-flow Hazard of area, Zhouqu County and differentiates, obtains the triple channel Detection results composite diagram after the set of doubtful landslide image block;

Fig. 5 utilizes connected region detection method for using in method described in the embodiment of the present invention, obtain in the doubtful landslide image block in area, Zhouqu County landslide seriously damage region after design sketch;

Fig. 6 is for using the extended range of acquisition landslide range of influence the rapid evaluation figure in the area, Zhouqu County after expanding that described in the embodiment of the present invention, method provides;

Zhouqu County rubble flow, Remote Sensing For Landslides surveillance map that Fig. 7 provides for existing Ministry of Civil Affairs's mitigation center;

Reference numeral:

1, landslide region; 2, landslide seriously damages region; 3, landslide generally damages region.

Embodiment

For making the object of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, clear, complete description is carried out to the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on embodiments of the invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.

According to theory of calamity definition, on the one hand, in landslide region, necessarily there is silt and the stone of not determined number, image shows as similarity and the regularity of local; On the other hand, silt or stone etc. have certain continuity in the existence in landslide region.Thus, we propose the landslide method for detecting area based on rarefaction representation classification SRC, comprise based on rarefaction representation classification SRC doubtful rubble flow method for detecting area, seriously to damage method for detecting area based on the landslide of eight neighborhood and generally damage method for detecting area based on the landslide of information before rarefaction representation and calamity.

The method of rarefaction representation derives from human vision research.Research finds, in human visual system, different neuronal cell is for different stimulations, as color, texture, yardstick, direction etc. produce reaction, these neuronal cells form a complete dictionary of mistake thus represent various visual effect, and this expression is sparse.Meanwhile, if the image of certain type things (or signal) has similarity, so build dictionary with the typical image sample of the type things, linear expression can be carried out to testing image, the principle of Here it is rarefaction representation.If set up the sub-dictionary of dissimilar things, the rarefaction representation error of each sub-dictionary to testing image just can be utilized to classify to it, differentiate, this is the ultimate principle of classification (SRC) method based on rarefaction representation.

Fig. 1 shows the schematic flow sheet of the landslide method for detecting area based on rarefaction representation classification that one embodiment of the invention provides, and as shown in Figure 1, the landslide method for detecting area based on rarefaction representation classification of the present embodiment is as described below.

101, target area testing image is divided into the identical M of a size testing image block, M be greater than 1 integer.

Will be understood that, with the pane of pre-set dimension, testing image Y can be divided into M testing image block, obtain testing image set of blocks wherein, R, G, B are respectively red R, green G, blue triple channel component.

102, the method utilizing rarefaction representation to classify is to the three-channel brightness analysis of described testing image block red, green, blue and differentiation, to obtain the set of the doubtful landslide image block in testing image block, and obtain the average of the difference of preset first threshold value and rarefaction representation error.

Will be understood that, observe landslide debris stream picture, what can find landslide region has certain unitarity in color, can the brightness of different color channels be analyzed, be differentiated thus, thus obtain the set of the doubtful landslide image block in testing image block, and obtain the average of the difference of preset first threshold value and rarefaction representation error.

103, utilize connected region detection method, in the set of described doubtful landslide image block, obtain landslide seriously damage region and zone boundary thereof.

104, according to the position of each point on described zone boundary, the land type of each point before calamity in image is obtained.

105, according to the land type of described each point before calamity in image and disaster-stricken loss weight corresponding to default land type, the influence coefficient of degree of expansion corresponding to each point is obtained.

It should be noted that disaster-stricken loss weight corresponding to the described default land type of this step 105 is arranged according to expertise.

106, according to the influence coefficient of the average of described preset first threshold value and the difference of rarefaction representation error and degree of expansion corresponding to each point, obtain the extended range in landslide region, and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region.

Will be understood that, according to the analysis to landslide region, the range of influence having certain limit around major disaster region can be found, and this scope is relevant with land type, thus, in conjunction with testing result after land use type before calamity and calamity, the extended range of landslide range of influence can be obtained.

The landslide method for detecting area based on rarefaction representation classification of the present embodiment, the method of being classified by rarefaction representation obtains the set of doubtful landslide image block, the landslide that connected region detection method obtains in testing image seriously damages region and zone boundary thereof, the land type of each point before calamity in image and the influence coefficient to degree of expansion of its correspondence is obtained according to various point locations on described zone boundary, the extended range in landslide region is obtained according to preset first threshold value and the average of the difference of rarefaction representation error and the influence coefficient of the corresponding degree of expansion of each point, and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region, efficient detection can be carried out to the landslide region in the Aerial Images to be measured of target area, accuracy is high, false alarm rate is low.

In a particular application, above-mentioned steps 102 can comprise not shown step 102a-102h:

102a, by testing image block carry out vectorization, normalization successively.

102b, built complete dictionary A according to the typical sample of red, green, blue triple channel component ^q(q=R, G, B), wherein, R, G, B are respectively red, green, blue triple channel component.

Will be understood that, by typical image sample vector, then that these are vectorial as atom one by one, carry out arrangement set, just constitute a matrix, this matrix is referred to as dictionary by us.Wherein matrix column number represents sample number, i.e. atom number.Suppose there is dictionary A ∈ R ^mxnif m<n, then claim this dictionary to be complete,

y＝Ax

Wherein, y is pending image block (or signal), and A is in advance according to the dictionary that typical sample establishes, and is complete, and x is linear coefficient vector.In x vector, the number of non-zero element is few as much as possible, namely has openness.When given dictionary, the object of rarefaction representation selects coefficient vector sparse as far as possible most, carrys out reconstructed image block y.

If set up the sub-dictionary of dissimilar things, the rarefaction representation error of each sub-dictionary to testing image just can be utilized to classify to it, differentiate, this is the ultimate principle of classification (SRC) method based on rarefaction representation.Because the embodiment of the present invention only needs to detect landslide region, and without the need to detecting other atural object classification, therefore, only set up the dictionary of this type of landslide.The embodiment of the present invention first chooses the landslide image block of some typicalness as sample, these image block vectorizations assembled a matrix, then matrix is carried out Gaussian number projection, namely obtain the dictionary that we want.

Testing image block after 102c, acquisition vectorization and normalization rarefaction representation coefficient

In a particular application, for example, above-mentioned steps 102c can be preferably:

Orthogonal matching pursuit OMP (orthogonal matching pursuit is called for short OMP) algorithm is utilized to obtain the testing image block after vectorization and normalization rarefaction representation coefficient it specifically can comprise:

According to the testing image block after vectorization and normalization with red, green, blue Three Channel Color component dictionary A ^q(q=R, G, B), establishing target function:

$\begin{array}{ccc}{\hat{x}}^q=\arg \min {\left|\right|x^q\left|\right|}_1& s.t.& A^q{x}^q=y_p^q(q=R,G,B;p=\mathrm{1,2},...,M);\end{array}$

Solve objective function, obtain the testing image block after vectorization and normalization rarefaction representation coefficient

102d, according to described rarefaction representation coefficient to described testing image block be reconstructed, obtain rarefaction representation error (namely reconstructing redundancy).

In a particular application, above-mentioned steps 102d can comprise:

According to described rarefaction representation coefficient by the first formula to described testing image block be reconstructed, obtain rarefaction representation error

Described first formula is:

$r_p^q\left(y_p^q\right)={\left|\right|y_p^q-A^q{\hat{x}}^q\left|\right|}_2(q=R,G,B;p=\mathrm{1,2},...,M).$

102e, according to described rarefaction representation error obtain preset first threshold value with rarefaction representation error difference

Wherein,

102f, general compare with default Second Threshold, during>=default Second Threshold, determine testing image block red/green/blue channel component image block is doubtful landslide image block.

In a particular application, above-mentioned steps 102f can also comprise:

? when < presets Second Threshold, determine testing image block red/green/blue channel component image block is non-landslide image block.

For example, the default Second Threshold in this step 102f can be 0, can utilize pre-set identification and classification device be, by testing image block be divided into two classes:

$\mathrm{identity}\left(y_p^q\right)=\left\{\begin{array}{ccc}1& \mathrm{if}& {\mathrm{\&lambda;}}_p^q\&GreaterEqual;0\\ 0& \mathrm{if}& {\mathrm{\&lambda;}}_p^q<0\end{array}\right.$

Wherein, 1 represents image block be judged as doubtful landslide image block, 0 represents image block be judged as non-landslide image block.

102g, at testing image block red, green, blue triple channel component image block when being doubtful landslide image block, determine that this testing image block is the doubtful image block of landslide, and then obtain the set of doubtful landslide image block.

Will be understood that, for example, if the default Second Threshold in above-mentioned steps 102f is 0, and utilize pre-set identification and classification device time, by testing image block be divided into two classes:

$\mathrm{identity}\left(y_p^q\right)=\left\{\begin{array}{ccc}1& \mathrm{if}& {\mathrm{\&lambda;}}_p^q\&GreaterEqual;0\\ 0& \mathrm{if}& {\mathrm{\&lambda;}}_p^q<0\end{array}\right.$

Wherein, 1 represents image block be judged as doubtful landslide image block, 0 represents image block be judged as non-landslide image block;

Then can at identity (Y in this step 102g _pduring)=1, determine that this testing image block is the doubtful image block of landslide, and then obtain the set { identity (Y of doubtful landslide image block _p)=1};

Wherein,

Described preset first threshold value when 102h, acquisition testing image block are landslide doubtful image block with rarefaction representation error difference average

Wherein, ${\stackrel{\&OverBar;}{\mathrm{\&lambda;}}}_p=\stackrel{q}{\mathrm{\&Sigma;}}{\mathrm{\&lambda;}}_p^q/3(q=R,G,B).$

In a particular application, for example, above-mentioned steps 103 can be preferably:

Utilize the connected region detection method of eight neighborhood method, in the set of described doubtful landslide image block, obtain landslide seriously damage region and zone boundary thereof, as shown in Figure 2, it specifically can comprise:

Using the central point of each doubtful landslide image block as a gauge point (i, j);

From each gauge point, each direction along eight neighborhood is searched for s time, if find other gauge points, then the party is upwards all designated as gauge point between original tally point and newfound gauge point;

Carry out communication with detection to all gauge points obtained, in doubtful landslide image block, the landslide obtained in testing image seriously damages region;

Wherein, the value of s depends on the size of gauge point position, target area image size and doubtful landslide image block.

In a particular application, this step 106 can comprise:

According to the influence coefficient of the average of described preset first threshold value and the difference of rarefaction representation error and degree of expansion corresponding to each point, extended range l (the i in landslide region is obtained by the 5th formula, j), and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region;

Wherein, described 5th formula is:

$l(i,j)={\stackrel{\&OverBar;}{\mathrm{\&lambda;}}}_p\&times;{\mathrm{\&theta;}}_n,$

for the average of the difference of preset first threshold value and rarefaction representation error, θ _nfor n-th kind of land type corresponding to each point on described zone boundary is to the influence coefficient of degree of expansion.

Through to choose Zhouqu County area Debris-flow Hazard after Aerial Images (i.e. Fig. 3) use method described in the present embodiment to detect, obtain design sketch as Figure 4-Figure 6, Fig. 4 shows and uses the method utilizing rarefaction representation to classify in method described in the present embodiment of the present invention to carry out red to the Aerial Images block after the Debris-flow Hazard of area, Zhouqu County, green, blue three-channel brightness is carried out analyzing and differentiating, obtain the triple channel Detection results composite diagram after the set of doubtful landslide image block, Fig. 5 shows to use and utilizes connected region detection method in method described in the embodiment of the present invention, Zhouqu County area doubtful landslide image block in obtain landslide seriously damage region after design sketch, Fig. 6 shows and uses the extended range of acquisition landslide range of influence the rapid evaluation figure in the area, Zhouqu County after expanding that described in the embodiment of the present invention, method provides, Fig. 7 shows the Zhouqu County rubble flow that existing Ministry of Civil Affairs's mitigation center provides, Remote Sensing For Landslides surveillance map, Fig. 6 and Fig. 7 is compared, through statistics, the detection accuracy that Fig. 6 detects the landslide region, area, Zhouqu County obtained is 83.35%, false alarm rate is 2.43%, be 6240 × 6431 in experiment test image size, when win7 64, I52.9GHz 4G memory machines is tested, the whole flow process used time is about 55min.

The landslide method for detecting area based on rarefaction representation classification of the present embodiment, can carry out efficient detection to the landslide region in the Aerial Images to be measured of target area, accuracy is high, and false alarm rate is low.

One of ordinary skill in the art will appreciate that: all or part of step realizing above-mentioned each embodiment of the method can have been come by the hardware that programmed instruction is relevant.Aforesaid program can be stored in a computer read/write memory medium.This program, when performing, performs the step comprising above-mentioned each embodiment of the method; And aforesaid storage medium comprises: ROM, RAM, magnetic disc or CD etc. various can be program code stored medium.

Last it is noted that above each embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to foregoing embodiments to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein some or all of technical characteristic; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the scope of the claims in the present invention.

Claims (10)

1., based on a landslide method for detecting area for rarefaction representation classification, it is characterized in that, comprising:

Target area testing image is divided into the identical M of a size testing image block, M be greater than 1 integer;

The method utilizing rarefaction representation to classify is analyzed the three-channel brightness of described testing image block red, green, blue and differentiates, to obtain the set of the doubtful landslide image block in testing image block, and obtain the average of the difference of preset first threshold value and rarefaction representation error;

Utilize connected region detection method, in the set of described doubtful landslide image block, obtain landslide seriously damage region and zone boundary thereof;

According to the position of each point on described zone boundary, obtain the land type of each point before calamity in image;

According to the land type of described each point before calamity in image and disaster-stricken loss weight corresponding to default land type, obtain the influence coefficient of degree of expansion corresponding to each point;

According to the influence coefficient of the average of described preset first threshold value and the difference of rarefaction representation error and degree of expansion corresponding to each point, obtain the extended range in landslide region, and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region.

2. method according to claim 1, it is characterized in that, the described method utilizing rarefaction representation to classify is to the three-channel brightness analysis of described testing image block red, green, blue and differentiation, to obtain the set of the doubtful landslide image block in testing image block, and obtain the average of the difference of preset first threshold value and rarefaction representation error, comprising:

By testing image block carry out vectorization, normalization successively;

Complete dictionary A was built according to the typical sample of red, green, blue triple channel component ^q(q=R, G, B), wherein, R, G, B are respectively red, green, blue triple channel component;

Obtain the testing image block after vectorization and normalization rarefaction representation coefficient

According to described rarefaction representation coefficient to described testing image block be reconstructed, obtain rarefaction representation error

According to described rarefaction representation error obtain preset first threshold value with rarefaction representation error difference

Will compare with default Second Threshold, when presetting Second Threshold, determine testing image block red/green/blue channel component image block is doubtful landslide image block;

At testing image block red, green, blue triple channel component image block when being doubtful landslide image block, determine that this testing image block is the doubtful image block of landslide, and then obtain the set of doubtful landslide image block;

Described preset first threshold value when acquisition testing image block is landslide doubtful image block with rarefaction representation error difference average

3. method according to claim 2, is characterized in that, the testing image block after described acquisition vectorization and normalization rarefaction representation coefficient comprise:

Testing image block after utilizing orthogonal matching pursuit OMP algorithm to obtain vectorization and normalization rarefaction representation coefficient

4. method according to claim 3, is characterized in that, described utilize orthogonal matching pursuit algorithm OMP algorithm to obtain vectorization and normalization after testing image block rarefaction representation coefficient comprise:

According to the testing image block after vectorization and normalization with red, green, blue Three Channel Color component dictionary A ^q(q=R, G, B), establishing target function:

${\hat{x}}^q=\arg \min {\left|\right|x^q\left|\right|}_1,s.t.A^q{x}^q=y_p^q(q=R,G,B;p=\mathrm{1,2},..,M);$

Solve objective function, obtain the testing image block after vectorization and normalization rarefaction representation coefficient

5. method according to claim 2, is characterized in that, described according to described rarefaction representation coefficient to described testing image block be reconstructed, obtain rarefaction representation error comprise:

According to described rarefaction representation coefficient by the first formula to described testing image block be reconstructed, obtain rarefaction representation error

Described first formula is:

$r_p^q\left(y_p^q\right)={\left|\right|y_p^q-A^q{\hat{x}}^q\left|\right|}_2(q=R,G,B;p=\mathrm{1,2},...,M).$

6. method according to claim 2, is characterized in that, described general compare with default Second Threshold, also comprise:

? time, determine testing image block red/green/blue channel component image block is non-landslide image block.

7. the method according to claim 5 or 6, is characterized in that, described default Second Threshold is 0.

8. method according to claim 1, is characterized in that, describedly utilizes connected region detection method, obtains landslide and seriously damages region and zone boundary thereof, comprising in the set of described doubtful landslide image block:

Utilize the connected region detection method of eight neighborhood method, in the set of described doubtful landslide image block, obtain landslide seriously damage region and zone boundary thereof.

9. method according to claim 8, is characterized in that, the described connected region detection method utilizing eight neighborhood method, obtains landslide and seriously damages region and zone boundary thereof, comprising in the set of described doubtful landslide image block:

Using the central point of each doubtful landslide image block as a gauge point (i, j);

From each gauge point, each direction along eight neighborhood is searched for s time, if find other gauge points, then the party is upwards all designated as gauge point between original tally point and newfound gauge point;

Communication with detection is carried out to all gauge points obtained, in doubtful landslide image block, obtains landslide seriously damage region;

Wherein, the value of s depends on the size of gauge point position, target area image size and doubtful landslide image block.

10. method according to claim 9, it is characterized in that, the influence coefficient of the described average according to described preset first threshold value and the difference of rarefaction representation error and degree of expansion corresponding to each point, obtain the extended range in landslide region, and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region, comprising:

According to the influence coefficient of the average of described preset first threshold value and the difference of rarefaction representation error and degree of expansion corresponding to each point, extended range l (the i in landslide region is obtained by the 5th formula, j), and utilize this extended range seriously to damage region to landslide to expand, obtain landslide and generally damage region;

Wherein, described 5th formula is:

$l(i,j)={\stackrel{\&OverBar;}{\mathrm{\&lambda;}}}_p\&times;{\mathrm{\&theta;}}_n,$

for the average of the difference of preset first threshold value and rarefaction representation error, θ _nfor n-th kind of land type corresponding to each point on described zone boundary is to the influence coefficient of degree of expansion.