A method and device for enhancing road connectivity based on remote sensing images

Abstract

The present invention provides a method and device for enhancing road connectivity based on remote sensing images, which relate to the technical field of image processing. The method and device include: acquiring a remote sensing image of an area to be processed, and segmenting the remote sensing image to obtain multiple sub-remote sensing images; extracting skeletons of the multiple sub-remote sensing images based on a morphological processing algorithm; splitting the skeleton into multiple line segments, and determining attribute data of the line segments, wherein the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment; connecting target endpoints based on the attribute data to obtain multiple target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range; constructing a raster image of the area to be processed based on the multiple target images and the multiple sub-remote sensing images, thereby solving the technical problem of poor connectivity of roads extracted by existing road extraction methods.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method and device for enhancing road connectivity based on remote sensing images. Background Art

[0002] Roads are important basic geographic data for map drawing, route search, and emergency response. They have a significant impact on urban construction, GIS system updates, military reconnaissance, and digital cities. Therefore, the rapid extraction of road features has important applications in urban development planning, electronic informatization, and other aspects.

[0003] At present, road extraction methods can be generally divided into three categories. The first category is knowledge-based road extraction methods, such as threshold method and template matching method; the second category is morphology-based methods, such as edge detection method, watershed algorithm, split-growth method; the third category is machine learning-based road extraction methods, such as clustering, support vector machine, Bayesian classification, neural network, etc. The road extraction effects of the first two categories of methods are not ideal, and the overall accuracy is low. Machine learning-based methods, especially those based on convolutional neural networks in recent years, can achieve good results using some labeled training samples, but they can only extract urban main roads or relatively wide roads. The extraction effect of rural roads or narrow paths in towns and villages is average. In addition, due to interference from shadows, buildings, trees and other objects, the extracted roads are often intermittent and have poor connectivity, which makes it difficult to meet actual application needs.

[0004] For the above problems, no effective solutions have been proposed yet. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and device for enhancing road connectivity based on remote sensing images, so as to alleviate the technical problem of poor connectivity of roads extracted by existing road extraction methods.

[0006] In a first aspect, an embodiment of the present invention provides a method for enhancing road connectivity based on remote sensing images, comprising: acquiring a remote sensing image of an area to be processed, and segmenting the remote sensing image to obtain a plurality of sub-remote sensing images; extracting the skeletons of the plurality of sub-remote sensing images based on a morphological processing algorithm; splitting the skeleton into a plurality of line segments, and determining the attribute data of the line segments, wherein the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment; connecting the target endpoints based on the attribute data to obtain a plurality of target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range; constructing a raster image of the area to be processed based on the plurality of target images and the plurality of sub-remote sensing images.

[0007] Furthermore, the remote sensing image is segmented to obtain a plurality of sub-remote sensing images, including: labeling the pixels contained in the remote sensing image to obtain a target remote sensing image, wherein the labeling is used to label the types of the pixels, and the types include: road pixels and background pixels; and slicing the target remote sensing image using a slicing algorithm to obtain the plurality of sub-remote sensing images.

[0008] Furthermore, based on a morphological processing algorithm, the skeletons of the multiple sub-remote sensing images are extracted, including: based on a morphological processing algorithm, determining a first target road in the sub-remote sensing image, wherein the first target road is a road in the sub-remote sensing image whose area is smaller than a preset area and whose perimeter is smaller than a preset perimeter; performing skeleton extraction on a second target road in the sub-remote sensing image to obtain the skeletons of the multiple sub-remote sensing images, wherein the second target road is a road in the sub-remote sensing image other than the first target road.

[0009] Furthermore, based on the attribute data, the target endpoints are connected to obtain multiple target images, including: calculating the Euclidean distance and angle difference between any two endpoints based on the attribute data; determining the target endpoints based on the Euclidean distance and the angle difference; and connecting the target endpoints using lines of preset width to obtain the multiple target images.

[0010] Furthermore, based on the multiple target images and the multiple sub-remote sensing images, a raster image of the area to be processed is constructed, including: fusing the multiple target images with the corresponding sub-remote sensing images to obtain multiple fused images; processing the multiple fused images based on a morphological processing algorithm to obtain multiple target fused images; and splicing the multiple target fused images to obtain a raster image of the area to be processed.

[0011] In a second aspect, an embodiment of the present invention provides a road connectivity enhancement device based on remote sensing images, comprising: an acquisition unit, an extraction unit, a splitting unit, a connection unit and a construction unit, wherein a remote sensing image of the area to be processed is acquired, and the remote sensing image is segmented to obtain a plurality of sub-remote sensing images; based on a morphological processing algorithm, the skeletons of the plurality of sub-remote sensing images are extracted; the skeletons are split into a plurality of line segments, and the attribute data of the line segments are determined, wherein the attribute data include: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment; based on the attribute data, the target endpoints are connected to obtain a plurality of target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range; based on the plurality of target images and the plurality of sub-remote sensing images, a raster image of the area to be processed is constructed.

[0012] Furthermore, the acquisition unit is used to: mark the pixels contained in the remote sensing image to obtain the target remote sensing image, wherein the marking is used to mark the type of the pixels, and the types include: road pixels and background pixels; and use a slicing algorithm to slice the target remote sensing image to obtain the multiple sub-remote sensing images.

[0013] Furthermore, the extraction unit is used to: determine the first target road in the sub-remote sensing image based on a morphological processing algorithm, wherein the first target road is a road in the sub-remote sensing image whose area is smaller than a preset area and whose perimeter is smaller than a preset perimeter; perform skeleton extraction on the second target road in the sub-remote sensing image to obtain the skeletons of the multiple sub-remote sensing images, wherein the second target road is a road in the sub-remote sensing image other than the first target road.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the method described in the first aspect above, and the processor is configured to execute the program stored in the memory.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored.

[0016] In an embodiment of the present invention, a remote sensing image of an area to be processed is acquired and the remote sensing image is segmented to obtain a plurality of sub-remote sensing images; based on a morphological processing algorithm, the skeletons of the plurality of sub-remote sensing images are extracted; the skeletons are split into a plurality of line segments, and the attribute data of the line segments are determined, wherein the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment; based on the attribute data, the target endpoints are connected to obtain a plurality of target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range; based on the plurality of target images and the plurality of sub-remote sensing images, a raster image of the area to be processed is constructed, thereby achieving the purpose of automatic enhanced processing of roads in remote sensing images, thereby solving the technical problem of poor connectivity of roads extracted by existing road extraction methods, and further achieving the technical effect of improving the connectivity of roads in remote sensing images.

[0017] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of a method for enhancing road connectivity based on remote sensing images provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of a road connectivity enhancement device based on remote sensing images provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] Example 1:

[0025] According to an embodiment of the present invention, an embodiment of a method for enhancing road connectivity based on remote sensing images is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 1 is a flow chart of a method for enhancing road connectivity based on remote sensing images according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0027] Step S102, obtaining a remote sensing image of the area to be processed, and segmenting the remote sensing image to obtain a plurality of sub-remote sensing images;

[0028] Step S104, extracting skeletons of the plurality of remote sensing sub-images based on a morphological processing algorithm;

[0029] Step S106: split the skeleton into multiple line segments and determine attribute data of the line segments, wherein the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment;

[0030] Step S108: connecting target endpoints based on the attribute data to obtain multiple target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range;

[0031] Step S110 : constructing a raster image of the area to be processed based on the multiple target images and the multiple sub-remote sensing images.

[0032] In an embodiment of the present invention, a remote sensing image of an area to be processed is acquired and the remote sensing image is segmented to obtain a plurality of sub-remote sensing images; based on a morphological processing algorithm, the skeletons of the plurality of sub-remote sensing images are extracted; the skeletons are split into a plurality of line segments, and the attribute data of the line segments are determined, wherein the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment; based on the attribute data, the target endpoints are connected to obtain a plurality of target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range; based on the plurality of target images and the plurality of sub-remote sensing images, a raster image of the area to be processed is constructed, thereby achieving the purpose of automatic enhanced processing of roads in remote sensing images, thereby solving the technical problem of poor connectivity of roads extracted by existing road extraction methods, and further achieving the technical effect of improving the connectivity of roads in remote sensing images.

[0033] In this embodiment of the present invention, step S102 includes the following steps:

[0034] Step S11, annotating pixels contained in the remote sensing image to obtain a target remote sensing image, wherein the annotation is used to annotate the types of the pixels, and the types include: road pixels and background pixels;

[0035] Step S12: Slice the target remote sensing image using a slicing algorithm to obtain the multiple sub-remote sensing images.

[0036] In an embodiment of the present invention, first, pixels contained in a remote sensing image are labeled, wherein a road pixel value is 1 and a background pixel value is 0, thereby obtaining a target remote sensing image.

[0037] Then, the target remote sensing image is sliced ​​to obtain multiple sub-remote sensing images.

[0038] After obtaining multiple remote sensing sub-images, each remote sensing sub-image is traversed from left to right and from top to bottom with a certain step size and executed from step S104 to step S108. This method can avoid the boundary misalignment problem that occurs when the sub-images are spliced.

[0039] In this embodiment of the present invention, step S104 includes the following steps:

[0040] Step S21, determining a first target road in the sub-remote sensing image based on a morphological processing algorithm, wherein the first target road is a road in the sub-remote sensing image having an area smaller than a preset area and a perimeter smaller than a preset perimeter;

[0041] Step S22 , performing skeleton extraction on the second target road in the sub-remote sensing image to obtain skeletons of the multiple sub-remote sensing images, wherein the second target road is a road in the sub-remote sensing image other than the first target road.

[0042] In this embodiment of the present invention, morphological operations are used to remove road features with too small an area and perimeter (i.e., the first target road). Closing operations are then used to enclose finely connected features. Finally, skeleton extraction is performed on the second target road. Skeleton extraction involves thinning a connected region to a single pixel width for feature extraction and target topology representation.

[0043] In this embodiment of the present invention, step S108 includes the following steps:

[0044] Step S31, calculating the Euclidean distance and angle difference between any two endpoints based on the attribute data;

[0045] Step S32, determining a target endpoint based on the Euclidean distance and the angle difference;

[0046] Step S33 , connecting the target endpoints using lines of preset width to obtain the multiple target images.

[0047] In the embodiment of the present invention, the skeleton is split into several line segments, each line segment has a unique direction, and the coordinates of the points in each line segment and the coordinates of the endpoints are recorded. Each line segment has two endpoints, and the coordinate sequence of each point on the line segment is recorded [p1, p2, p3, p4, ..., p n ], where p1, p n are the coordinates of the endpoints, for any i=1,2,…,n-1,p i ,p i+1 are the coordinates of two adjacent points.

[0048] Set the distance threshold τ and the road width γ, calculate the angle of each endpoint on the line segment, and connect two endpoints whose distance is less than τ and whose angle difference is within a certain range λ with a width of γ. The specific calculation method is as follows:

[0049] (1) Calculate the angle A(p) of each endpoint p(x1,y1) on the edge, with a value range of (-90,90], and its specific expression is as follows:

[0050]

[0051] Where (x2, y2) is a point on the edge where the endpoint p is located and has a certain distance from p, such as the edge [p1, p2, p3, p4, ..., p n ] to find the direction of p1, the default is to use p 10 .

[0052] (2) For any two endpoints p(x1,y1) and q(x2,y2) on the small graph, calculate the Euclidean distance between them

[0053]

[0054] (3) If the distance between the two end points is less than τ, and the angle satisfies the following formula:

[0055] |A(p)-A(q)|<λ, then connect the two points with a width of γ.

[0056] In this embodiment of the present invention, step S110 includes the following steps:

[0057] Step S41, fusing the multiple target images with corresponding sub-remote sensing images to obtain multiple fused images;

[0058] Step S42, processing the multiple fused images based on a morphological processing algorithm to obtain multiple target fused images;

[0059] Step S43: stitching the multiple target fusion images to obtain a raster image of the area to be processed.

[0060] In an embodiment of the present invention, multiple target images are first fused with corresponding sub-remote sensing images one by one to obtain multiple fused images, wherein the pixel values ​​at corresponding positions between the target image and the corresponding sub-remote sensing image are ORed to obtain new small-block images. Then, morphological processing is performed on the multiple fused images to fill holes, remove debris, etc., to obtain multiple target fused images.

[0061] Finally, multiple target fusion images are spliced ​​to obtain a raster image of the area to be processed.

[0062] The embodiment of the present invention is used to enhance the connectivity of road extraction results. A complete processing flow from "road extraction results - morphological processing - skeleton graph splitting into segments - connecting breakpoints - morphological processing - result output" has been developed, realizing the full process of automated enhancement processing of remote sensing image roads.

[0063] Example 2:

[0064] An embodiment of the present invention also provides a road connectivity enhancement device based on remote sensing images, which is used to execute the road connectivity enhancement method for remote sensing images provided in the above content of the embodiment of the present invention. The following is a detailed introduction to the road connectivity enhancement device for remote sensing images provided in the embodiment of the present invention.

[0065] like Figure 2 As shown, Figure 2 Schematic diagram of the road connectivity enhancement device for the remote sensing image, the building extraction device includes: an acquisition unit 10, an extraction unit 20, a splitting unit 30, a connection unit and a construction unit 50.

[0066] The acquisition unit 10 is used to acquire a remote sensing image of the area to be processed and segment the remote sensing image to obtain a plurality of sub-remote sensing images;

[0067] The extraction unit 20 is configured to extract skeletons of the plurality of remote sensing sub-images based on a morphological processing algorithm;

[0068] The splitting unit 30 is used to split the skeleton into multiple line segments and determine attribute data of the line segments, wherein the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment;

[0069] The connecting unit 40 is configured to connect target endpoints based on the attribute data to obtain a plurality of target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range;

[0070] The construction unit 50 is configured to construct a raster image of the area to be processed based on the multiple target images and the multiple sub-remote sensing images.

[0071] In an embodiment of the present invention, a remote sensing image of an area to be processed is acquired and the remote sensing image is segmented to obtain a plurality of sub-remote sensing images; based on a morphological processing algorithm, the skeletons of the plurality of sub-remote sensing images are extracted; the skeletons are split into a plurality of line segments, and the attribute data of the line segments are determined, wherein the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment; based on the attribute data, the target endpoints are connected to obtain a plurality of target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range; based on the plurality of target images and the plurality of sub-remote sensing images, a raster image of the area to be processed is constructed, thereby achieving the purpose of automatic enhanced processing of roads in remote sensing images, thereby solving the technical problem of poor connectivity of roads extracted by existing road extraction methods, and further achieving the technical effect of improving the connectivity of roads in remote sensing images.

[0072] Example 3:

[0073] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the method described in the above embodiment 1, and the processor is configured to execute the program stored in the memory.

[0074] See also Figure 3 An embodiment of the present invention further provides an electronic device 100, comprising: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0075] The memory 61 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 63 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0076] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0077] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0078] The processor 60 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method may be performed by hardware integrated logic circuits or software instructions within the processor 60. The processor 60 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention may be directly executed by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software modules may be located in storage media well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory 61 , and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0079] Example 4:

[0080] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the above embodiment 1 are executed.

[0081] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0082] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0085] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0086] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A road connectivity enhancement method based on remote sensing images, characterized in that: include: Acquire a remote sensing image of the area to be processed, and segment the remote sensing image to obtain a plurality of sub-remote sensing images; Extracting skeletons of the plurality of remote sensing sub-images based on a morphological processing algorithm; Splitting the skeleton into multiple line segments and determining attribute data of the line segments, wherein each line segment has a unique direction, and the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment; Based on the attribute data, target endpoints are connected to obtain multiple target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range; constructing a raster image of the area to be processed based on the multiple target images and the multiple sub-remote sensing images; Based on the multiple target images and the multiple sub-remote sensing images, a raster image of the area to be processed is constructed, including: fusing the multiple target images with the corresponding sub-remote sensing images to obtain multiple fused images; processing the multiple fused images based on a morphological processing algorithm to obtain multiple target fused images; and splicing the multiple target fused images to obtain a raster image of the area to be processed.

2. The method according to claim 1, characterized in that The remote sensing image is segmented to obtain a plurality of sub-remote sensing images, including: Annotating pixels contained in the remote sensing image to obtain a target remote sensing image, wherein the annotation is used to mark the type of the pixels, and the type includes: road pixels and background pixels; The target remote sensing image is sliced ​​using a slicing algorithm to obtain the multiple sub-remote sensing images.

3. The method according to claim 1, characterized in that Extracting skeletons of the plurality of remote sensing sub-images based on a morphological processing algorithm includes: Determining a first target road in the sub-remote sensing image based on a morphological processing algorithm, wherein the first target road is a road in the sub-remote sensing image having an area smaller than a preset area and a perimeter smaller than a preset perimeter; A skeleton is extracted from the second target road in the sub-remote sensing image to obtain skeletons of the multiple sub-remote sensing images, wherein the second target road is a road in the sub-remote sensing image other than the first target road.

4. The method according to claim 1, wherein Based on the attribute data, target endpoints are connected to obtain multiple target images, including: Based on the attribute data, calculating the Euclidean distance and angle difference between any two endpoints; Determining a target endpoint based on the Euclidean distance and the angle difference; The target endpoints are connected using lines of preset width to obtain the multiple target images.

5. A road connectivity enhancement device based on remote sensing images, characterized in that: include: Get unit, extract unit, split unit, join unit and build unit, where, The acquisition unit is used to acquire a remote sensing image of the area to be processed and segment the remote sensing image to obtain a plurality of sub-remote sensing images; The extraction unit is configured to extract the skeletons of the plurality of remote sensing sub-images based on a morphological processing algorithm; The splitting unit is used to split the skeleton into multiple line segments and determine attribute data of the line segments, wherein each line segment has a unique direction, and the attribute data includes: the direction of the line segment, the coordinates of the points contained in the line segment, and the coordinates of the endpoints of the line segment; The connecting unit is configured to connect target endpoints based on the attribute data to obtain a plurality of target images, wherein the target endpoints are two endpoints whose Euclidean distance is less than a preset distance and whose angle difference is within a preset range; The construction unit is configured to construct a raster image of the area to be processed based on the multiple target images and the multiple sub-remote sensing images; The construction unit is specifically used to: fuse the multiple target images with the corresponding sub-remote sensing images to obtain multiple fused images; process the multiple fused images based on a morphological processing algorithm to obtain multiple target fused images; and splice the multiple target fused images to obtain a raster image of the area to be processed.

6. The device according to claim 5, characterized in that The acquisition unit is used for: Annotating pixels contained in the remote sensing image to obtain a target remote sensing image, wherein the annotation is used to mark the type of the pixels, and the type includes: road pixels and background pixels; The target remote sensing image is sliced ​​using a slicing algorithm to obtain the multiple sub-remote sensing images.

7. The device according to claim 5, characterized in that The extraction unit is used to: Determining a first target road in the sub-remote sensing image based on a morphological processing algorithm, wherein the first target road is a road in the sub-remote sensing image having an area smaller than a preset area and a perimeter smaller than a preset perimeter; A skeleton is extracted from the second target road in the sub-remote sensing image to obtain skeletons of the multiple sub-remote sensing images, wherein the second target road is a road in the sub-remote sensing image other than the first target road.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store a program for supporting the processor to execute the method according to any one of claims 1 to 4, and the processor is configured to execute the program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are executed.

Images