CN107316296B - Remote sensing image change detection method and device based on logarithmic transformation - Google Patents

Remote sensing image change detection method and device based on logarithmic transformation Download PDF

Info

Publication number
CN107316296B
CN107316296B CN201710537712.0A CN201710537712A CN107316296B CN 107316296 B CN107316296 B CN 107316296B CN 201710537712 A CN201710537712 A CN 201710537712A CN 107316296 B CN107316296 B CN 107316296B
Authority
CN
China
Prior art keywords
image
remote sensing
difference image
sensing image
difference
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201710537712.0A
Other languages
Chinese (zh)
Other versions
CN107316296A (en
Inventor
周文艳
贾振红
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xinjiang University
Original Assignee
Xinjiang University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xinjiang University filed Critical Xinjiang University
Priority to CN201710537712.0A priority Critical patent/CN107316296B/en
Publication of CN107316296A publication Critical patent/CN107316296A/en
Application granted granted Critical
Publication of CN107316296B publication Critical patent/CN107316296B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/34—Smoothing or thinning of the pattern; Morphological operations; Skeletonisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20032—Median filtering

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Computation (AREA)
  • Evolutionary Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Astronomy & Astrophysics (AREA)
  • Remote Sensing (AREA)
  • Quality & Reliability (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

The invention discloses a remote sensing image change detection method and device based on logarithmic transformation, relates to the technical field of image processing, and mainly aims to solve the problem that the performance index of a detection result of the change detection of a remote sensing image by using the existing unsupervised image change detection method is still low, so that the accuracy of the change detection of the remote sensing image is low. The technical scheme comprises the following steps: acquiring a first remote sensing image and a second remote sensing image, wherein the first remote sensing image and the second remote sensing image are respectively corresponding to two different time phases; calculating a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by respectively utilizing a preset first function and a preset second function; calculating a final difference image from the first difference image and the second difference image, the final difference image being an image representing a variation area between the two different time phases. The method is mainly used for detecting the change area of the two-time phase remote sensing image.

Description

Remote sensing image change detection method and device based on logarithmic transformation
Technical Field
The invention relates to the technical field of image processing, in particular to a remote sensing image change detection method and device based on logarithmic transformation.
Background
With the rapid development of remote sensing technology, remote sensing images have been widely used in a variety of fields, such as land use monitoring, forest monitoring, city monitoring, natural disaster assessment and analysis, and the like. The change detection of the remote sensing image refers to determining and analyzing the change information of the earth surface of the region according to two or more remote sensing images acquired by the same region at different moments. Generally, methods for detecting changes of remote sensing images can be divided into two categories, namely, a supervised detection method and an unsupervised detection method. The supervision detection method is a method for training a sample by using a classifier and then detecting a change area of a remote sensing image according to the trained sample; the non-rough-reduction detection method is a method for directly detecting two or more remote sensing images. However, the supervised detection method needs to acquire real reference data, and the cost is high, so that the unsupervised detection method is more common in practical application.
At present, when the change detection of the remote sensing image is carried out by using an unsupervised detection method, the accuracy of the detection result of the remote sensing image is improved by the unsupervised image change detection method of data fusion and fuzzy clustering, or the unsupervised image change detection algorithm of PPB (pre-processing point) denoising and K-means clustering, but the performance index of the detection result is still lower, so that the accuracy of the change detection of the remote sensing image is lower.
Disclosure of Invention
In view of the above problems, the present invention is provided to provide a method and a device for detecting a change of a remote sensing image based on logarithmic transformation, and mainly aims to solve the problem that the performance index of the detection result of the change detection of the remote sensing image by the existing unsupervised image change detection method is still low, thereby causing the accuracy of the change detection of the remote sensing image to be low.
By means of the technical scheme, the remote sensing image change detection method based on logarithmic transformation comprises the following steps:
acquiring a first remote sensing image and a second remote sensing image;
calculating a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by respectively utilizing a preset first function and a preset second function;
and calculating a final difference image according to the first difference image and the second difference image.
By means of the technical scheme, the remote sensing image change detection device based on logarithmic transformation comprises:
the acquisition unit is used for acquiring a first remote sensing image and a second remote sensing image;
the calculation unit is used for calculating a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by respectively utilizing a preset first function and a preset second function;
the calculating unit is further configured to calculate a final difference image according to the first difference image and the second difference image.
Compared with the existing method for detecting the change of the remote sensing image through the unsupervised image change detection, the method and the device for detecting the change of the remote sensing image based on the logarithmic transformation provided by the embodiment of the invention have the advantages that the remote sensing images corresponding to two different time phases in the same area are obtained, the two different difference images are obtained by respectively calculating through two preset functions according to the two obtained remote sensing images, the final difference image is calculated according to the two obtained difference images, the performance index of the detection result is improved, and the accuracy of the remote sensing image change detection is further improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a method for detecting changes in remote sensing images based on logarithmic transformation according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method for detecting the change of the remote sensing image based on the logarithmic transformation, which is provided by the embodiment of the invention;
FIG. 3 is a block diagram of a remote sensing image change detection device based on logarithmic transformation according to an embodiment of the invention;
fig. 4 shows a block diagram of another remote sensing image change detection device based on logarithmic transformation according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides a remote sensing image change detection method based on logarithmic transformation, as shown in figure 1, the method comprises the following steps:
101. and acquiring a first remote sensing image and a second remote sensing image.
The first remote sensing image and the second remote sensing image are remote sensing images corresponding to two different time phases in the same area, and the remote sensing images can be Synthetic Aperture Radar (SAR) images.
For example, when the first remote sensing image is acquired as the SAR image X of the region a in the time phase 11，X1＝{X1(i, j), wherein i is more than 1 and less than m, j is more than 1 and less than n, and the SAR image obtained when the second remote sensing image is the time phase 2 of the area A is X2，X2＝{X2(i，j)，1＜i＜m，1＜j＜n}，X1And X2SAR images are all m pixels by n pixels.
102. And calculating a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by respectively utilizing a preset first function and a preset second function.
The preset first function may be a difference function, and the second preset function may be a logarithmic ratio function, for example, as the first remote sensing image X acquired in step 1011And a second remote sensing image X2Then, the specific calculation of the difference image by using the difference function and the logarithm ratio function can be as follows:
the first difference image is F1(i，j)＝|X1(i，j)-X2(i, j) |, the second difference image being
Figure BDA0001336228770000031
In the formula, G represents1(i, j) +1 in place of G1(i, j) with G2(i, j) +1 in place of G2(i, j) to avoid the occurrence of Gi(i is 1, 2) in the case where the pixel value is 0.
103. And calculating a final difference image according to the first difference image and the second difference image.
Wherein the final difference image is an image representing a variation area between the two different phases.
Specifically, the step 103 may assign weights to the first difference image and the second difference image respectively, and then calculate a final difference image according to the two difference images and the weights respectively corresponding to the two difference images.
For the embodiment of the invention, the remote sensing images of two time phases in the same area are obtained, and the operations such as processing, qualitative analysis, calculation and the like are carried out according to the obtained remote sensing images of the two time phases, so that the difference remote sensing image, namely the image of the change area between the two time phases, is finally obtained, and the change detection of the remote sensing image is completed.
Compared with the existing method for detecting the change of the remote sensing image through the unsupervised image change detection, the method for detecting the change of the remote sensing image based on the logarithmic transformation has the advantages that the remote sensing images corresponding to two different time phases in the same area are obtained, two different difference images are obtained through calculation by using two preset functions according to the two obtained remote sensing images, and the final difference image is calculated according to the two obtained difference images, so that the performance index of a detection result is improved, and the accuracy of the remote sensing image change detection is improved.
The embodiment of the invention also provides another remote sensing image change detection method based on logarithmic transformation, as shown in fig. 2, the method comprises the following steps:
201. and acquiring a first remote sensing image and a second remote sensing image.
The first remote sensing image and the second remote sensing image are remote sensing images corresponding to two different time phases in the same area respectively.
This step is the same as step 101 shown in fig. 1, and is not described herein again.
202. And carrying out logarithmic transformation on the first remote sensing image and the second remote sensing image respectively.
Further, a log domain-based first image corresponding to the first remote sensing image and a log domain-based second image corresponding to the second remote sensing image are obtained. Wherein the logarithmic conversion may be a base-2 logarithmic operation.
Specifically, the step 202 may be as follows:
U1(i，j)＝lb(X1(i，j)+1)，U2(i，j)＝lb(X2(i, j) +1), wherein X1And X2Remote sensing images, U, of two different time phases respectively1And U2Respectively representing the images based on the logarithmic domain obtained by carrying out logarithmic conversion on the remote sensing images. In the formula, X is used1(i, j) +1 in place of X1(i，j)，X2(i, j) +1 in place of X2(i, j) to avoid the occurrence of Xi(i is 1, 2) in the case where the pixel value is 0.
For the embodiment of the invention, the remote sensing image is converted into the logarithm domain image by carrying out logarithm conversion on the obtained remote sensing image, so that the noise in the remote sensing image is compressed while the gray value of the remote sensing image is compressed to be between [0 and 8], further the remote sensing image is denoised, and the precision of change detection of the remote sensing image is improved.
203. And denoising the first image based on the logarithmic domain and the second image based on the logarithmic domain.
Further, a denoised first image and a denoised second image are obtained respectively. Specifically, the step 203 may be to denoise the log-transformed log-domain-based image through a Frost filter.
According to the embodiment of the invention, the image based on the logarithmic domain is denoised by using the Frost filter, so that the edge information of the image can be kept while the noise is smoothed, the denoised remote sensing image is more accurate, and the accuracy of the change detection of the remote sensing image is improved.
204. And calculating a first difference image and a second difference image according to the denoised first image and the denoised second image by respectively utilizing a preset first function and a preset second function.
The preset first function may be a difference function, the second preset function may be a logratio function, and the detailed concept explanation may refer to the corresponding description in step 102, which is not described herein again.
It should be noted that, the influence of speckle noise can be eliminated to a certain extent by denoising and performing difference operation on the image based on the logarithmic domain, and the degree of identification of the changing region can be improved and the background information of the non-changing region can be suppressed by denoising and performing logarithmic ratio operation on the image based on the logarithmic domain, because the image has stronger robustness, the accuracy of the change detection of the remote sensing image is improved.
205. And respectively carrying out filtering processing on the first difference image and the second difference image.
Further, a filtered first difference image and a filtered second difference image are obtained. The filtering process may be an average filter process or a median filter process, and the first difference image and the second difference image may be the same filtering process method or different filtering process methods, which is not limited in the embodiment of the present invention.
206. And calculating a final difference image according to the filtered first difference image and the filtered second difference image.
Specifically, for the embodiment of the present invention, the step 206 may specifically include: configuring a first weight coefficient and a second weight coefficient for the filtered first difference image and the filtered second difference image respectively; respectively calculating a product of the first weight coefficient and the first difference image and a product of the second weight coefficient and the second difference remote sensing image to obtain a first product and a second product; and determining a sum value obtained by adding the first product and the second product as a final difference remote sensing image. It should be noted that, in order to ensure that the gray-level value of the final difference image is still between 0 and 8, the sum of the first weight coefficient and the second weight coefficient is 1.
For the embodiment of the present invention, specific application scenarios may be as follows, but are not limited to the following scenarios, including:
obtaining a first remote sensing image X1And a second remote sensing image X2For the remote sensing image X1And X2Carrying out logarithmic conversion to obtain an image U based on a logarithmic domain1And U2Then to the image U1And U2Denoising to obtain a denoised image G1And G2From image G1Calculating by using a difference method to obtain a first difference image F1From image G2Calculating by using a logarithmic ratio method to obtain a second difference image F2And then the first difference image F is filtered by an average filter1Filtering to obtain a first difference image D1The second difference image F is filtered by a median filter2Filtering to obtain a filtered first difference image D2In order to balance the smooth image and preserve the image details, the first weight coefficient and the second weight coefficient may be set to equal values, i.e. both are 0.5, and then the final difference remote sensing image R (i, j) may be calculated as:
R(i，j)＝0.5D1(i，j)+0.5D2(i，j)。
207. and clustering the final difference remote sensing images by using a preset clustering algorithm.
The preset clustering algorithm may be an FCM (Flow Cytometry/fine capacity model).
Specifically, the step 207 may be performed by minimizing the objective function JmFinding fuzzy clustering of given data, minimizing target function JmComprises the following steps:
Figure BDA0001336228770000061
wherein Y is (Y)1，y2，...，yn) Is a set of n-dimensional data samples, Z ═ Z1，z2，...，zn) Is the clustering center of the fuzzy group I, U ═ Uij]c×nIs a membership matrix of Y, uij∈[0，1]Is yiMembership degree, | y belonging to the i-th classj-ziI is the Euclidean distance between the ith cluster center and the jth data point, and m is the (1, ∞)]Is a weighted index, and then J can be obtained by Lagrange multipliermNecessary condition for reaching minimum value
Figure BDA0001336228770000071
And
Figure BDA0001336228770000072
and (5) performing iteration to obtain an optimal clustering result.
Compared with the conventional method for detecting the change of the remote sensing image through the unsupervised image change, the method for detecting the change of the remote sensing image based on the logarithmic transformation has the advantages that the remote sensing images in two different time phases in the same area are obtained, the two remote sensing images are subjected to logarithmic transformation and denoising, difference images are respectively calculated for the two denoised images through a difference function and a logarithmic ratio function, the difference images are subjected to filtering processing, the final difference remote sensing image is calculated according to the configured weight coefficient, the performance index of a detection result is improved, and the accuracy of the remote sensing image change detection is further improved.
Further, as a specific implementation of the method shown in fig. 1, an embodiment of the present invention provides a remote sensing image change detection apparatus based on logarithmic transformation, and as shown in fig. 3, the apparatus may include: an acquisition unit 31 and a calculation unit 32.
An acquiring unit 31 configured to acquire a first remote sensing image and a second remote sensing image; the obtaining unit 31 is a functional module for executing the obtaining of the first remote sensing image and the second remote sensing image by the remote sensing image change detecting device based on logarithmic transformation.
A calculation unit 32, configured to calculate a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by using a preset first function and a preset second function, respectively; the calculation unit 32 is a functional module for calculating the first difference image and the second difference image by the remote sensing image change detection device based on logarithmic transformation.
The calculating unit 32 may be further configured to calculate a final difference image according to the first difference image and the second difference image; the calculation unit 32 is a functional module for calculating the final difference image by using the remote sensing image change detection device based on logarithmic transformation.
The embodiment of the apparatus corresponds to the embodiment of the method, and for convenience of reading, details in the embodiment of the apparatus are not repeated one by one, but it should be clear that the apparatus in the embodiment can correspondingly implement all the contents in the embodiment of the method.
Compared with the existing remote sensing image change detection method through an unsupervised image change detection method, the remote sensing image change detection device based on logarithmic transformation provided by the embodiment of the invention has the advantages that the remote sensing images corresponding to two different time phases in the same area are obtained, two different difference images are obtained through calculation by using two preset functions according to the two obtained remote sensing images, and the final difference image is calculated according to the two obtained difference images, so that the performance index of a detection result is improved, and the accuracy of remote sensing image change detection is further improved.
Further, as a specific implementation of the method shown in fig. 2, an embodiment of the present invention provides another remote sensing image change detection apparatus based on logarithmic transformation, and as shown in fig. 4, the apparatus may include: an acquisition unit 41 and a calculation unit 42.
An obtaining unit 41 configured to obtain a first remote sensing image and a second remote sensing image;
a calculating unit 42, configured to calculate a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by using a preset first function and a preset second function, respectively;
the calculating unit 42 is further configured to calculate a final difference image according to the first difference image and the second difference image.
Further, the apparatus further comprises: a conversion unit 43 and a denoising unit 44.
The conversion unit 43 is configured to perform logarithmic conversion on the first remote sensing image and the second remote sensing image respectively.
The denoising unit 44 is configured to denoise the log domain-based first image and the log domain-based second image.
Further, the calculating unit 42 is specifically configured to calculate the first difference image and the second difference image according to the denoised first image and the denoised second image by using a preset first function and a preset second function, respectively.
Further, the apparatus further comprises: a filtering unit 45.
The filtering unit 45 is configured to perform filtering processing on the first difference image and the second difference image respectively.
Further, the calculating unit 42 is specifically configured to calculate a final difference image according to the filtered first difference image and the filtered second difference image.
Specifically, in order to improve the accuracy of the remote sensing image change detection, the calculation unit 42 includes:
a configuration module 4201, configured to configure the first filtered difference image and the second filtered difference image with a first weighting coefficient and a second weighting coefficient, respectively;
a calculation module 4202, configured to calculate a product of the first weight coefficient and the first difference image, and a product of the second weight coefficient and the second difference remote sensing image, respectively, to obtain a first product and a second product;
a determining module 4203, configured to determine a sum of the first product and the second product as a final difference remote sensing image.
Further, the apparatus further comprises: a clustering unit 46.
And the clustering unit 46 is configured to cluster the final difference remote sensing images by using a preset clustering algorithm.
The embodiment of the apparatus corresponds to the embodiment of the method, and for convenience of reading, details in the embodiment of the apparatus are not repeated one by one, but it should be clear that the apparatus in the embodiment can correspondingly implement all the contents in the embodiment of the method.
Compared with the conventional method for detecting the change of the remote sensing image through the unsupervised image change, the device for detecting the change of the remote sensing image based on the logarithmic transformation provided by the embodiment of the invention has the advantages that the remote sensing images in two different time phases in the same area are obtained, the two remote sensing images are subjected to logarithmic transformation and denoising, then the difference images are respectively calculated for the two denoised images by using the difference function and the logarithmic ratio function, the difference images are subjected to filtering processing, and the final difference remote sensing image is calculated according to the configured weight coefficient, so that the performance index of a detection result is improved, and the accuracy of the change detection of the remote sensing images is further improved.
The remote sensing image change detection device based on logarithmic transformation comprises a processor and a memory, wherein the acquisition unit, the calculation unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem that the performance index of the detection result of the change detection of the remote sensing image by the existing non-supervised image change detection method is still low, so that the accuracy of the change detection of the remote sensing image is low is solved by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The present application further provides a computer program product adapted to perform program code for initializing the following method steps when executed on a data processing device: acquiring a first remote sensing image and a second remote sensing image, wherein the first remote sensing image and the second remote sensing image are respectively corresponding to two different time phases; calculating a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by respectively utilizing a preset first function and a preset second function; calculating a final difference image from the first difference image and the second difference image, the final difference image being an image representing a variation area between the two different time phases.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (6)

1. A remote sensing image change detection method based on logarithmic transformation is characterized by comprising the following steps:
acquiring a first remote sensing image and a second remote sensing image, wherein the first remote sensing image and the second remote sensing image are remote sensing images respectively corresponding to two different time phases in the same area;
respectively carrying out logarithmic transformation on the first remote sensing image and the second remote sensing image to obtain a log domain-based first image corresponding to the first remote sensing image and a log domain-based second image corresponding to the second remote sensing image, and compressing the gray values of the first remote sensing image and the second remote sensing image to be between [0,8 ];
denoising the first image based on the logarithmic domain and the second image based on the logarithmic domain through a Frost filter to respectively obtain a denoised first image and a denoised second image;
calculating a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by respectively utilizing a preset first function and a preset second function;
respectively performing filtering processing on the first difference image and the second difference image, wherein the filtering processing is mean filter processing or median filter processing to obtain a filtered first difference image and a filtered second difference image;
calculating a final difference image according to the filtered first difference image and the filtered second difference image, wherein the final difference image is a change area image representing the two different time phases;
the calculating the first difference image and the second difference image according to the first remote sensing image and the second remote sensing image by respectively utilizing a preset first function and a preset second function comprises the following steps:
and calculating a first difference image and a second difference image according to the denoised first image and the denoised second image by respectively utilizing a preset first function and a preset second function.
2. The method of claim 1, wherein computing a final difference image from the filtered first difference image and the filtered second difference image comprises:
configuring a first weight coefficient and a second weight coefficient for the filtered first difference image and the filtered second difference image respectively;
respectively calculating a product of the first weight coefficient and the filtered first difference image and a product of the second weight coefficient and the filtered second difference image to obtain a first product and a second product;
and determining a sum value of the first product and the second product as a final difference image.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
and clustering the final difference images by using a preset clustering algorithm.
4. A remote sensing image change detection device based on logarithmic transformation is characterized by comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a first remote sensing image and a second remote sensing image, and the first remote sensing image and the second remote sensing image are respectively corresponding remote sensing images in two different time phases in the same area;
the conversion unit is used for respectively carrying out logarithmic conversion on the first remote sensing image and the second remote sensing image to obtain a first image based on a logarithmic domain corresponding to the first remote sensing image and a second image based on the logarithmic domain corresponding to the second remote sensing image, and compressing the gray values of the first remote sensing image and the second remote sensing image to be between [0,8 ];
the denoising unit is used for denoising the first image based on the logarithmic domain and the second image based on the logarithmic domain through a Frost filter to respectively obtain a denoised first image and a denoised second image;
the calculation unit is used for calculating a first difference image and a second difference image according to the first remote sensing image and the second remote sensing image by respectively utilizing a preset first function and a preset second function;
the filtering unit is used for respectively performing filtering processing on the first difference image and the second difference image, wherein the filtering processing is mean filter processing or median filter processing to obtain a first difference image after filtering and a second difference image after filtering;
the calculating unit is further configured to calculate a final difference image according to the filtered first difference image and the filtered second difference image, where the final difference image is a change area image representing the two different time phases;
the calculating unit is specifically configured to calculate a first difference image and a second difference image according to the denoised first image and the denoised second image by using a preset first function and a preset second function, respectively.
5. The apparatus of claim 4, wherein the computing unit comprises: a configuration module, a calculation module, a determination module,
the configuration module is configured to configure a first weight coefficient and a second weight coefficient for the filtered first difference image and the filtered second difference image respectively;
the calculating module is configured to calculate a product of the first weight coefficient and the filtered first difference image, and a product of the second weight coefficient and the filtered second difference image, respectively, to obtain a first product and a second product;
the determining module is configured to determine a sum of the first product and the second product as a final difference image.
6. The apparatus of claim 4 or 5, further comprising:
and the clustering unit is used for clustering the final difference image by utilizing a preset clustering algorithm.
CN201710537712.0A 2017-06-29 2017-06-29 Remote sensing image change detection method and device based on logarithmic transformation Active CN107316296B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710537712.0A CN107316296B (en) 2017-06-29 2017-06-29 Remote sensing image change detection method and device based on logarithmic transformation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710537712.0A CN107316296B (en) 2017-06-29 2017-06-29 Remote sensing image change detection method and device based on logarithmic transformation

Publications (2)

Publication Number Publication Date
CN107316296A CN107316296A (en) 2017-11-03
CN107316296B true CN107316296B (en) 2020-11-10

Family

ID=60181125

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710537712.0A Active CN107316296B (en) 2017-06-29 2017-06-29 Remote sensing image change detection method and device based on logarithmic transformation

Country Status (1)

Country Link
CN (1) CN107316296B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108830828B (en) * 2018-04-28 2022-02-18 新疆大学 Method and device for detecting change of remote sensing image
CN109583487A (en) * 2018-11-21 2019-04-05 新疆大学 A kind of SAR image change detection and device
CN109886959B (en) * 2019-03-19 2023-04-25 新疆大学 Method and device for detecting image change
CN111383216B (en) * 2020-03-10 2022-04-05 新疆大学 Method and device for detecting change between images

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103456020B (en) * 2013-09-08 2016-04-13 西安电子科技大学 Based on the method for detecting change of remote sensing image of treelet Fusion Features
CN103456018B (en) * 2013-09-08 2017-01-18 西安电子科技大学 Remote sensing image change detection method based on fusion and PCA kernel fuzzy clustering
CN103955894B (en) * 2014-04-14 2017-04-19 武汉科技大学 Medical ultrasound image speckle removing method through quantum inspiration
CN104200471B (en) * 2014-08-30 2017-03-01 西安电子科技大学 SAR image change detection based on adaptive weight image co-registration
CN104778717A (en) * 2015-05-05 2015-07-15 西安电子科技大学 SAR image change detection method based on oriented difference chart
CN106296655B (en) * 2016-07-27 2019-05-21 西安电子科技大学 SAR image change detection based on adaptive weight and high frequency threshold value

Also Published As

Publication number Publication date
CN107316296A (en) 2017-11-03

Similar Documents

Publication Publication Date Title
CN107316296B (en) Remote sensing image change detection method and device based on logarithmic transformation
WO2023138300A1 (en) Target detection method, and moving-target tracking method using same
US8015132B2 (en) System and method for object detection and classification with multiple threshold adaptive boosting
US8559726B2 (en) Image analysis based on pixel brightness grouping
US20180144182A1 (en) Analyzing digital holographic microscopy data for hematology applications
CN112446378B (en) Target detection method and device, storage medium and terminal
CN108830828B (en) Method and device for detecting change of remote sensing image
CN110363165B (en) Multi-target tracking method and device based on TSK fuzzy system and storage medium
CN111383216B (en) Method and device for detecting change between images
CN114821102A (en) Intensive citrus quantity detection method, equipment, storage medium and device
CN108564083A (en) A kind of method for detecting change of remote sensing image and device
CN109903246B (en) Method and device for detecting image change
CN111814852A (en) Image detection method, image detection device, electronic equipment and computer-readable storage medium
CN115439456A (en) Method and device for detecting and identifying object in pathological image
CN117557820A (en) Quantum dot optical film damage detection method and system based on machine vision
CN112348750A (en) SAR image change detection method based on threshold fusion and neighborhood voting
CN116612103A (en) Intelligent detection method and system for building structure cracks based on machine vision
CN112133100B (en) Vehicle detection method based on R-CNN
Wenyan et al. SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain
CN113688655B (en) Method, device, computer equipment and storage medium for identifying interference signals
Wang et al. An image edge detection algorithm based on multi-feature fusion
CN113792764A (en) Sample expansion method, system, storage medium and electronic equipment
Kuang et al. Object tracking with collaborative extreme learning machines
CN112330666B (en) Image processing method, system, device and medium based on improved twin network
CN117218496A (en) Detection model training method, image processing method and electronic equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant