A method for estimating and classifying landform morphological boundaries based on spatial composition structure information

Abstract

The present invention discloses a method for estimating and classifying geomorphic form boundaries based on spatial composition structure information, which relates to the technical field of digital geomorphic form classification and includes the following steps: Step 1, extract geomorphic form elements; Step 2, obtain spatial composition structure information to form a two-dimensional sample space; Step 3, based on the formed two-dimensional sample space, estimate the geomorphic form boundaries to obtain corresponding boundary lines and segmented regions; Step 4, classify and map the regions segmented by the boundary lines according to the topological relationship of the spatial composition structure information of the geomorphic forms; The method provided by the present invention makes full use of the differences in the spatial composition structure information between geomorphic form types and the prior expert knowledge such as the spatial distribution and topological relationship of geomorphic form types, without considering issues such as the selection of geomorphic form factors and the optimal analysis scale. The overall process is simple, effective and highly operable.

Description

Technical Field

[0001] The present invention relates to the technical field of digital geomorphic form classification, and in particular to a method for estimating and classifying geomorphic form boundaries based on spatial composition structure information. Background Art

[0002] Geomorphic form is an important description content of geomorphic types and is crucial for revealing the causes, evolution processes of landforms, and applications such as geomorphic mapping. With the continuous enrichment of raster DEM (Digital Elevation Model) data, digital geomorphic form analysis methods based on this have also been continuously developed, which provides a technical basis for the automatic classification of geomorphic forms.

[0003] Currently, there are various methods for the automatic classification of geomorphic forms. Generally speaking, they are mainly divided into three categories: clustering-based methods, rule-based knowledge methods, and typical sample point-based methods. These traditional methods can basically meet the automatic classification of basic geomorphic form types, that is, geomorphic form elements. However, they perform poorly in the automatic classification application of composite geomorphic forms with multi-level relationships and strong spatial heterogeneity characteristics. On the one hand, it is difficult for traditional methods to effectively establish a reasonable attribute space set or give a clear constraint rule knowledge for automatic classification. On the other hand, traditional methods also have a common defect, that is, they lack sufficient consideration of the spatial composition structure and cannot reflect the spatial hierarchical combination relationship of geomorphic forms. Therefore, traditional methods are difficult to be applied to the automatic classification of composite geomorphic forms. Therefore, there is an urgent need for a method for estimating and classifying geomorphic form boundaries based on spatial composition structure information to overcome the problems existing in the existing methods. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for estimating and classifying geomorphic form boundaries based on spatial composition structure information, so as to solve the problems existing in the prior art that it is difficult to effectively establish a reasonable attribute space set or give a clear constraint rule knowledge for automatic classification and it is difficult to reflect the spatial hierarchical combination relationship of geomorphic forms.

[0005] To achieve the above purpose, the present invention provides a method for estimating and classifying geomorphic form boundaries based on spatial composition structure information, including the following steps:

[0006] Step 1: Extract geomorphic form elements;

[0007] Step 2: Obtain spatial composition structure information to form a two-dimensional sample space;

[0008] Step 3: Based on the two-dimensional sample space formed in Step 2, estimate the geomorphic form boundary to obtain the corresponding boundary line and segmentation area;

[0009] Step 4: Classify and map the regions divided by the boundary line according to the topological relationship of the spatial composition structure information of the geomorphic form.

[0010] Preferably, the process of extracting geomorphic form elements in Step 1 is as follows:

[0011] S11: Set several analysis scale windows of different scales, and then identify terrain elements through each analysis scale window;

[0012] S12: Construct a set of element classification results for each position according to the identification results of the terrain elements in S11;

[0013] S13: Adopt the comprehensive evaluation method, and determine the final element type of each position according to the principle that the element appears most frequently. The expression is as follows:

[0014] (1)

[0015] In the formula, represents the analysis scale, represents the element type at the single analysis scale at the position under the analysis scale, represents the final element type at the position .

[0016] Preferably, the process of obtaining the spatial composition structure information and constructing a two-dimensional sample space in Step 2 is as follows:

[0017] S21: Outline a rough dividing line at the position where the boundary may exist according to the expert's prior knowledge, establish a buffer zone of this dividing line, and limit the calculation area;

[0018] S22: Obtain the element type and elevation information of each position in the fuzzy boundary buffer zone;

[0019] S23: Store the element types at the positions with the same elevation as a sub-information set respectively;

[0020] S24: Arrange the sub-information sets at each elevation in the order of elevation change to form a two-dimensional sample space.

[0021] Preferably, based on the two-dimensional sample space formed in Step 2, the process of estimating the geomorphic form boundary and obtaining the corresponding boundary line and divided regions in Step 3 is as follows:

[0022] S31: Calculate the information entropy of the sub-information sets of the elements at the same elevation respectively, and realize the transformation of the two-dimensional information in the two-dimensional sample space into one-dimensional information;

[0023] S32. Set different elevations as the boundary threshold values, and calculate the second-order information entropy of the information entropy values on both sides of the boundary threshold respectively;

[0024] S33. Calculate the sum of the second-order information entropy on both sides of the boundary threshold;

[0025] S34. Traverse and calculate the one-dimensional information generated in S31 in the order of elevation, and use the elevation line with the largest sum of the second-order information entropy values on both sides as the corresponding boundary line.

[0026] Preferably, the expression for calculating the information entropy of the subset of elements at the same elevation in S31 is as follows:

[0027] (2)

[0028] In the formula, is the element type at the same elevation, is the total number of element types at the same elevation, is the element type The probability statistics of, represents the information entropy composed of elements at the same elevation, represents the total number of elements with the element type of M.

[0029] Preferably, the expression for calculating the second-order information entropy of the information entropy values on both sides of the boundary threshold in S32 is as follows:

[0030] (3)

[0031] In the formula, is the total number of the information entropy composed of elements on one side of the boundary threshold, is the information entropy composed of elements The probability statistics of, represents the second-order information entropy of the element composition on one side of the boundary threshold, represents the total number of elements with the information entropy of E(M).

[0032] Preferably, the expression for calculating the sum of the second-order information entropy on both sides of the boundary threshold in S33 is as follows:

[0033] (4)

[0034] In the formula, represents the entropy value less than the boundary threshold , represents the entropy value greater than the boundary threshold , represents the boundary threshold of The sum of the entropy values at that time; in addition, the calculation result of formula (4) can also give the spatial distribution of the uncertainty of the boundary position estimation results under different threshold conditions.

[0035] Preferably, the topological relationship of the spatial composition structure information of the geomorphic form is specifically spatial adjacency and spatial inclusion.

[0036] Therefore, the present invention adopts the above-mentioned method for estimating and classifying the geomorphic form boundary based on spatial composition structure information, and has the following beneficial effects:

[0037] (1) The method proposed by the present invention realizes the division of geomorphic form types from the perspective of the differences in the spatial composition structure between composite geomorphic form types on the basis of considering the spatial composition structure of geomorphic form types; a large number of geomorphic form factors do not need to be derived to construct a complete set of terrain attributes or knowledge rules during the whole process, so a series of problems such as the selection of geomorphic form factors and the analysis scale of suitability do not need to be considered; relatively speaking, the method of the present invention is the simplest and most effective.

[0038] (2) The present invention takes the line with the minimum uncertainty as the boundary line between types according to the boundary estimation result, then converts the range included in the boundary line into surface data, and assigns morphological types to the surface data according to the prior knowledge of the spatial topological relationship of composite geomorphic types, and finally obtains the geomorphic form type classification map. The overall classification result is more in line with the actual situation; and the classification method of the present invention performs well in the classification application of composite geomorphic form types, can obtain an ideal geomorphic form classification result, and can meet the requirements of composite geomorphic mapping applications.

[0039] The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0040] Figure 1 It is the overall flow chart of a method for estimating and classifying the geomorphic form boundary based on spatial composition structure information of the present invention;

[0041] Figure 2 It is the setting diagram of the fuzzy boundary calculation area in the embodiment of the present invention. Among them, (a) is the set fuzzy boundary diagram, and (b) is the composition diagram of the terrain elements in the two-sided geomorphic form types covered in the calculation area;

[0042] Figure 3 It is the two-dimensional sample space diagram of the element types in the fuzzy boundary area of the embodiment of the present invention;

[0043] Figure 4 It is the complexity distribution diagram of the element type composition at different elevations in the embodiment of the present invention;

[0044] Figure 5It is the estimation map of the demarcation position under different elevation threshold conditions of the embodiment of the present invention. Among them, (a) is the statistical distribution map of the second-order information entropy, and (b) is the uncertainty space distribution map of the demarcation position estimation;

[0045] Figure 6 It is the topological relationship map of the composite geomorphic form types of the embodiment of the present invention;

[0046] Figure 7 It is the research area distribution map of the embodiment of the present invention;

[0047] Figure 8 It is the estimation result map of the geomorphic form boundary position in the research area of the embodiment of the present invention. Among them, (a) is the classification result map of the element type, and (b) is the uncertainty space distribution map of the geomorphic form boundary position estimation;

[0048] Figure 9 It is the classification result map of the geomorphic form types in the research area of the embodiment of the present invention. Among them, (a) is the classification result map, and (b) is the correction result map. Detailed implementation manners

[0049] The following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0050] Please refer to Figures 1-9 , a method for estimating and classifying geomorphic form boundaries based on spatial composition structure information, comprising the following steps:

[0051] Step 1: Extract geomorphic form elements; for the discrimination of geomorphic form elements, the present invention uses the Geomorphons method to enumerate the geometric forms of the research area and establish a mapping relationship with the corresponding geomorphic form elements. On this basis, a multi-scale comprehensive discrimination is adopted for the element recognition results at different analysis scales, and according to the principle that the element appears most frequently, the final element type is determined; the specific process is as follows:

[0052] S11: Set several analysis scale windows of different scales, and then identify the terrain elements through each analysis scale window;

[0053] S12: Construct a set of element classification results for each position according to the terrain element recognition results in S11;

[0054] S13: Adopt a comprehensive evaluation method, and according to the principle that the element appears most frequently, determine the final element type of each position. The expression is as follows:

[0055] (1)

[0056] Wherein, represents the analysis scale, represents at the position the element type under a single analysis scale and represents at the position the final element type.

[0057] Step 2, obtain the spatial composition structure information to form a two-dimensional sample space; the specific process is as follows:

[0058] S21. According to the expert's prior knowledge, outline a rough dividing line at the possible boundary positions, establish a buffer zone for the dividing line, and limit the calculation area. Among them, the width of the buffer zone should be set to at least cover half of the internal area of the morphological type or ensure that the covered range can reflect the composition structure information of the element types inside the type, as Figure 2 shown. The dark solid line is the fuzzy boundary set according to the expert's prior knowledge, and the range circled by the light solid line is the fuzzy boundary calculation area set according to the fuzzy boundary and the width parameters d1 and d2;

[0059] S22. Obtain the element type and elevation information at each position within the fuzzy boundary buffer zone;

[0060] S23. Store the element types at the positions with the same elevation as a sub-information set respectively;

[0061] S24. Arrange the sub-information sets at each elevation in the order of elevation change to form a two-dimensional sample space, as Figure 3 shown.

[0062] Step 3, based on the two-dimensional sample space formed in Step 2, estimate the geomorphic form boundary to obtain the corresponding boundary line and segmentation area; the specific process is as follows:

[0063] S31. Calculate the information entropy for the sub-information sets of the elements at the same elevation respectively, and transform the two-dimensional information in the two-dimensional sample space into one-dimensional information, as Figure 4 shown; among them, the expression for calculating the information entropy for the sub-information sets of the elements at the same elevation respectively is as follows:

[0064] (2)

[0065] Wherein, is the element type at the same elevation, is the total number of element types at the same elevation, is the element type Probability statistics represents the information entropy composed of elements at the same elevation represents the total number of elements of type M

[0066] S32. Set different elevations as the boundary thresholds, and calculate the second-order information entropy of the information entropy values on both sides of the boundary thresholds respectively. The specific calculation expression is as follows:

[0067] (3)

[0068] In the formula, is the total number of the information entropy composed of elements on one side of the boundary threshold is the information entropy of the element composition Probability statistics represents the second-order information entropy of the element composition on one side of the boundary threshold represents the total number of elements with the information entropy of E(M) in the element composition

[0069] S33. Calculate the sum of the second-order information entropy on both sides of the boundary threshold. The specific calculation expression is as follows:

[0070] (4)

[0071] In the formula, represents the entropy value less than the boundary threshold represents the entropy value greater than the boundary threshold represents the sum of the entropy values when the boundary threshold is ; in addition, the calculation result of formula (4) can also give the uncertainty spatial distribution of the boundary position estimation results under different threshold conditions

[0072] S34. Traverse and calculate the one-dimensional information generated in S31 in the order of elevation, and take the elevation line with the largest sum of the second-order information entropy values on both sides as the corresponding boundary line, as Figure 5 shown. (a) is the statistical distribution of the sum of the second-order information entropy on both sides of the boundary threshold, and (b) is the uncertainty spatial distribution of the boundary position estimation. The color changes from red to blue, indicating that the uncertainty changes from low to high. Among them, the elevation threshold with the smallest uncertainty is the boundary position between types

[0073] Step 4. Classify and map the regions divided by the boundary line according to the topological relationship of the spatial composition structure information of the geomorphic form; among them, the topological relationship of the spatial composition structure information of the geomorphic form is specifically spatial adjacency and spatial inclusion. The present invention generalizes and defines the topological relationship of the spatial structure composition of the composite geomorphic form according to the description of the composite geomorphic structure and spatial hierarchy, and combines expert knowledge, as​​Figure 6 As shown in the figure, where 1 represents the reef front slope, 2 represents the reef flat, and 3 represents the lagoon; (1) represents the outer reef flat, (2) represents the reef bulge zone, (3) represents the inner reef flat, (4) represents the lagoon slope, (5) represents the lagoon bottom, and (6) represents the patch reef; I represents the coral sparse zone, II represents the coral thicket zone, and III represents the reef pit development zone. The spatial distributions of the three geomorphic types of reef front slope, reef flat, and lagoon all have the characteristics of an offshore sequence and a geomorphic type classification system with at least two levels, and the topological relationships between geomorphic types are mainly spatial adjacency and inclusion. According to the topological relationships of the composite geomorphic form types, the geomorphic forms divided by the estimated boundaries can be classified into definite geomorphic form types.

[0074] Embodiment

[0075] Taking the application of coral atoll geomorphic form classification as an example, the geomorphic form classification method proposed by the present invention is applied for analysis and accuracy evaluation.

[0076] The research area selected in this embodiment is a typical coral atoll located in the southern part of the Xisha Islands in the South China Sea, which includes three types of first-level geomorphic types, namely reef flat, lagoon, and patch reef. Among them, the reef flat only covers the coral thicket zone and the reef pit development zone, which are secondary geomorphic types close to the lagoon slope. The lagoon includes two secondary geomorphic types, the lagoon slope and the lagoon bottom. The morphological characteristics and terrain complexities of different geomorphic types vary greatly, and the four geomorphic types of coral thicket zone, lagoon slope, lagoon bottom, and patch reef are typical in terms of morphological characteristics and component compositions and are quite different from each other. Therefore, in this embodiment, some typical areas of the above four geomorphic types are selected as the test areas, such as Figure 7 As shown, it represents a DEM data. The elevations in the figure successively include the coral thicket zone, the lagoon slope, the lagoon bottom, and the patch reef from high to low; the specific geomorphic classification system and definition description of the research area are shown in Table 1:

[0077] Table 1 Geomorphic Classification System within the Coverage of the Research Area

[0078] ;

[0079] Results and Analysis:

[0080] 1. Results of Multi-scale Comprehensive Discrimination of Geomorphic Form Elements

[0081] The results of multi-scale comprehensive discrimination of geomorphic form elements in the research area are as Figure 8As shown in (a) of . The coral bioherm zone with relatively shallow water depth has a relatively high surface morphology complexity and a relatively fragmented elemental composition due to the widespread coral reefs. The lagoon bottom with relatively deep water depth also has a relatively fragmented elemental composition, but affected by the distribution of patch reefs, the distribution area of valley-shaped elements is relatively large. In addition, due to the uneven surface of the reef pit development zone, the elemental composition is also relatively fragmented. Compared with the reef pit development zone, the lagoon slope mainly consists of sandy clastic accumulations, so the elemental composition is relatively simple, mainly dominated by slopes. The patch reefs are generally hill-shaped, so the elemental types are also relatively simple, mainly slopes. From the above results, it can be seen that the spatial distribution of the elemental type composition has typical spatial differentiation characteristics. This spatial differentiation is consistent with the spatial distribution of the atoll geomorphic morphological types.

[0082] 2. Estimation Uncertainty Distribution Results of Each Demarcation Threshold

[0083] Based on the elemental identification results and combined with the prior knowledge of experts, the maximum second-order entropy estimation model was applied to estimate the fuzzy areas existing at the boundary positions, and the estimation uncertainty distribution results of each demarcation threshold were obtained, as Figure 8 shown in (b) of . At the position areas where obvious transitions occur in the elemental type composition, the boundary estimation uncertainty is relatively low. At the position areas where the changes in the elemental type composition are not obvious, the boundary estimation uncertainty is relatively high, especially inside the sand slopes where the substrate composition and morphology are relatively homogeneous. At the position areas with relatively weak spatial heterogeneity of the elemental composition, the high and low distributions of the boundary estimation uncertainty are relatively concentrated. At the position areas with relatively strong spatial heterogeneity of the elemental composition, the high and low distributions of the boundary estimation uncertainty are relatively dispersed. The estimation uncertainty results of the boundary between the inner part of the lagoon and the patch reefs show a discrete ring-shaped distribution, while the estimation uncertainty results of the boundary from the coral bioherm zone to the lagoon bottom show an intermittent high and low distribution. From the above results, it can also be seen that the spatial distribution law of the boundary position estimation uncertainty conforms to the characteristics and spatial topological relationships of the atoll geomorphic morphological types.

[0084] 3. Geomorphic Morphological Classification Results

[0085] In the present invention, the line with the minimum uncertainty is used as the demarcation line between types according to the boundary estimation results. Then, the range included in the demarcation line is converted into surface data, and morphological types are assigned to the surface data according to the prior knowledge of the spatial topological relationship of the atoll geomorphic types, and finally the geomorphic morphological type classification map is obtained, as Figure 9 shown in (a) of . From the coverage ranges of each type in the classification map, it can be seen that the overall classification results are in line with the actual situation. After the classification is completed, in this example, the sampling data obtained from the field investigation is used to correct and adjust the type boundary positions, and the final classification results are obtained, as Figure 9 shown in (b) of .

[0086] 4. Classification Accuracy Evaluation

[0087] In this example, a total of 27,233 sample points were selected based on field survey data and the boundary buffer range. The accuracy of the classification results was evaluated using a confusion matrix and Kappa coefficient, as shown in Table 2:

[0088] Table 2 Comparison of Classification Results and Sample Point Data

[0089] ;

[0090] As can be seen from Table 2, the mapping accuracy of point reefs, coral thicket zones, lagoon slopes, and lagoon bottoms reached over 93%, and the user accuracy was between 91% and 100%. The user accuracy of the reef pit development zone was 100%, and the mapping accuracy was 86%. The overall accuracy of the geomorphic form type classification result was 94%, and the Kappa coefficient was 0.91. The classification result was good. The above results indicate that the method of the present invention performs well in the classification application of coral reef complex geomorphic form types, can obtain ideal geomorphic form classification results, and can meet the requirements of geomorphic mapping.

[0091] In summary, in the classification application of complex geomorphic form types, the geomorphic form boundary estimation and classification method based on spatial composition information proposed by the present invention can maintain relatively ideal accuracy in the geomorphic form classification results and can meet the requirements of complex geomorphic mapping.

[0092] Therefore, the present invention adopts the above-mentioned geomorphic form boundary estimation and classification method based on spatial composition structure information, fully utilizes the differences in spatial composition structure information between geomorphic form types and expert prior knowledge such as the spatial distribution and topological relationship of geomorphic form types, and does not need to consider issues such as the selection of geomorphic form factors and the optimal analysis scale. The overall process is simple, effective, and highly operable.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for estimating and classifying landform boundaries based on spatial composition structure information, characterized in that: The following steps are involved: Step 1: Extract geomorphic elements; Step 2: Obtain spatial composition structure information to form a two-dimensional sample space; Step 3: Based on the two-dimensional sample space formed in step 2, estimate the landform boundary to obtain the corresponding boundary line and segmentation area; Step 4: classify and map the areas divided by the boundary lines according to the topological relationship of the spatial composition structure information of the landform; In step 2, the process of obtaining the spatial composition structure information and forming a two-dimensional sample space is as follows: S21. Outline an approximate dividing line at a location where a boundary may exist based on the expert's prior knowledge, establish a buffer zone for the dividing line, and limit the calculation area; S22, obtaining element type and elevation information of each position in the fuzzy boundary buffer; S23, storing the element types at the positions with the same elevation as a sub-information set respectively; S24, arranging the sub-information sets at each elevation in the order of elevation change to form a two-dimensional sample space; In step 3, based on the two-dimensional sample space constructed in step 2, the landform boundary is estimated to obtain the corresponding boundary line and segmentation area as follows: S31, respectively calculating information entropy for sub-information sets of elements located at the same elevation, so as to transform the two-dimensional information in the two-dimensional sample space into one-dimensional information; S32, setting different elevations as demarcation thresholds, and calculating the second-order information entropy for the information entropy values ​​on both sides of the demarcation threshold; S33, calculating the sum of the second-order information entropy on both sides of the demarcation threshold; S34, traversing and calculating the one-dimensional information generated in S31 in the order of elevation, and taking the elevation line with the maximum sum of the second-order information entropy values ​​on both sides as the corresponding boundary line.

2. A method for estimating and classifying landform boundaries based on spatial composition structure information according to claim 1, characterized in that: The process of extracting geomorphic elements in step 1 is as follows: S11, setting a number of analysis scale windows of different scales, and then identifying terrain elements through each analysis scale window; S12, constructing a set of element classification results for each position according to the recognition results of the terrain elements in S11; S13. Use the comprehensive evaluation method to determine the final element type at each position based on the principle of the element with the highest frequency of occurrence. The expression is as follows: （1） In the formula, represents the analysis scale, Indicates at location On a single scale of analysis The element type below, Indicates at location The final element type of the .

3. The method for estimating and classifying landform boundaries based on spatial composition structure information according to claim 2, characterized in that: The expressions for calculating the information entropy of the sub-information sets of elements at the same elevation in S31 are as follows: （2） In the formula, are the element types located at the same elevation, is the total number of element types at the same elevation, For element type The probability statistics of It represents the information entropy of the elements at the same elevation. Indicates the total number of elements of type M.

4. The method for estimating and classifying landform boundaries based on spatial composition structure information according to claim 3, characterized in that: In S32, the calculation expressions for the second-order information entropy of the information entropy values ​​on both sides of the demarcation threshold are as follows: （3） In the formula, is the total number of elements on one side of the dividing threshold that make up the information entropy, The information entropy of the elements The probability statistics of The information entropy of the second-order elements on one side of the dividing threshold is represented by The information entropy of element composition is The total quantity.

5. A method for estimating and classifying landform boundaries based on spatial composition structure information according to claim 4, characterized in that: The calculation expression of the sum of the second-order information entropy on both sides of the demarcation threshold in S33 is as follows: （4） In the formula, Indicates that it is less than the demarcation threshold The entropy value of Indicates that it is greater than the demarcation threshold The entropy value of The demarcation threshold is The sum of the entropy values ​​at time .

6. A method for estimating and classifying landform boundaries based on spatial composition structure information according to claim 5, characterized in that: The topological relationship of the spatial composition structure information of landforms is specifically spatial adjacency and spatial inclusion.

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