Hyperspectral image processing method and device, electronic equipment and storage medium

Abstract

The invention relates to a hyperspectral image processing method and device, electronic equipment and a storage medium, and the method comprises the steps: calculating a first estimation mean value and a first estimation covariance value of a partial hyperspectral image when the partial hyperspectral image is received; taking the first estimated mean value and the first estimated covariance value as estimated values of a global mean value and a global covariance value of a complete hyperspectral image corresponding to the partial hyperspectral image; and based on the global average value and the global covariance value, processing the complete hyperspectral image through a hyperspectral image processing algorithm to obtain a processing result. According to the method provided by the invention, the first estimated mean value and the first estimated covariance value of the partial hyperspectral image are used as the estimated values of the global mean value and the global covariance value of the complete hyperspectral image corresponding to the partial hyperspectral image, so that the complete hyperspectral image can be processed when only the partial hyperspectral image is received; and the time from capturing important data for the first time to processing and explaining the data is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of hyperspectral image processing, and in particular to a hyperspectral image processing method, device, electronic device and storage medium. Background Art

[0002] Most known hyperspectral imaging sensors can only record a thin line of the image (i.e. a row of data) at a time. A hyperspectral image is built by sweeping the sensor across the scene, for example, using a motorized stage or using the motion of an aircraft to sweep across the landscape (push-broom scanning) to get a complete hyperspectral image. Existing hyperspectral detection systems only begin processing after the entire hyperspectral image has been captured, which can take hours for sensors on aircraft or similar devices, greatly increasing the time from first capturing important data to processing and interpreting that data. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a hyperspectral image processing method, device, electronic device and storage medium, aiming to solve at least one of the above technical problems.

[0004] In the first aspect, the technical solution of the present invention to solve the above technical problem is as follows: a hyperspectral image processing method, the method comprising:

[0005] When receiving a portion of the hyperspectral image, calculating a first estimated mean value and a first estimated covariance value of the portion of the hyperspectral image;

[0006] Using the first estimated mean value and the first estimated covariance value as estimated values ​​of the global mean value and the global covariance value of the complete hyperspectral image corresponding to the partial hyperspectral image, respectively;

[0007] Based on the global average value and the global covariance value, the complete hyperspectral image is processed by a hyperspectral image processing algorithm to obtain a processing result.

[0008] The beneficial effect of the present invention is that the first estimated mean value and the first estimated covariance value of a partial hyperspectral image are used as estimated values ​​of the global mean value and the global covariance value of a complete hyperspectral image corresponding to the partial hyperspectral image, so that when only a partial hyperspectral image is received, the processing of the complete hyperspectral image can be realized, thereby reducing the time from the first capture of important data to the processing and interpretation of the data.

[0009] Based on the above technical solution, the present invention can also be improved as follows.

[0010] Furthermore, the method further comprises:

[0011] When receiving another part of the hyperspectral image except the part of the hyperspectral image in the complete hyperspectral image, calculating a second estimated mean value and a second estimated covariance value of the other part of the hyperspectral image;

[0012] Based on the second estimated mean value and the second estimated covariance value, the global average value and the global covariance value are updated to obtain a new global average value and a new global covariance value.

[0013] Furthermore, the partial hyperspectral image is acquired from a device for collecting hyperspectral images, or is acquired from a data storage space containing a complete hyperspectral image.

[0014] Furthermore, the method further comprises:

[0015] The first relevant data of the complete hyperspectral image and / or the second relevant data of the processing result are transmitted to a remote device, wherein the first relevant data includes the partial hyperspectral image and / or the another partial hyperspectral image.

[0016] Further, the partial hyperspectral image is at least one row of data in a complete hyperspectral image created by scanning the scene one row at a time.

[0017] In a second aspect, in order to solve the above technical problems, the present invention further provides a hyperspectral image processing device, the device comprising:

[0018] A first calculation module is used to calculate a first estimated mean value and a first estimated covariance value of the partial hyperspectral image when receiving the partial hyperspectral image;

[0019] A first determining module is used to use the first estimated mean value and the first estimated covariance value as estimated values ​​of a global mean value and a global covariance value of a complete hyperspectral image corresponding to the partial hyperspectral image, respectively;

[0020] The processing module is used to process the complete hyperspectral image through a hyperspectral image processing algorithm based on the global average value and the global covariance value to obtain a processing result.

[0021] In a third aspect, in order to solve the above-mentioned technical problem, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the hyperspectral image processing method of the present application is implemented.

[0022] In a fourth aspect, in order to solve the above technical problem, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the hyperspectral image processing method of the present application is implemented.

[0023] Additional aspects and advantages of the present application will be partially given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for describing the embodiments of the present invention are briefly introduced below.

[0025] Figure 1 A schematic diagram of a process of a hyperspectral image processing method provided by an embodiment of the present invention;

[0026] Figure 2 A system processing flow chart provided for one embodiment of the present invention;

[0027] Figure 3 A schematic flow chart of another hyperspectral image processing method provided by an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of the structure of a hyperspectral image processing device provided by one embodiment of the present invention;

[0029] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0031] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0032] The solution provided by the embodiment of the present invention can be applied to any application scenario that requires processing of a complete hyperspectral image. The solution provided by the embodiment of the present invention can be executed by any electronic device, for example, it can be a user's terminal device, including at least one of the following: a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0033] The embodiment of the present invention provides a possible implementation method, such as Figure 1As shown in FIG. 1 , a flowchart of a method for processing a hyperspectral image is provided. The method can be executed by any electronic device, for example, a terminal device, or a terminal device and a server. For the convenience of description, the method provided by the embodiment of the present invention will be described below by taking the terminal device as the execution subject as an example. Figure 1 The method may include the following steps:

[0034] S10, when receiving a portion of the hyperspectral image, calculating a first estimated mean value and a first estimated covariance value of the portion of the hyperspectral image;

[0035] S20, using the first estimated mean value and the first estimated covariance value as estimated values ​​of a global mean value and a global covariance value of a complete hyperspectral image corresponding to the partial hyperspectral image, respectively;

[0036] S30, based on the global average value and the global covariance value, the complete hyperspectral image is processed by a hyperspectral image processing algorithm to obtain a processing result.

[0037] By using the method of the present invention, the first estimated mean value and the first estimated covariance value of a partial hyperspectral image are used as estimated values ​​of the global mean value and the global covariance value of a complete hyperspectral image corresponding to the partial hyperspectral image. When only a partial hyperspectral image is received, the processing of the complete hyperspectral image can be achieved, thereby reducing the time from the first capture of important data to the processing and interpretation of the data.

[0038] The scheme of the present invention is further described below in conjunction with the following specific embodiments. In this embodiment, a hyperspectral image processing method provided may include the following steps:

[0039] S10, when receiving a portion of the hyperspectral image, calculating a first estimated mean value and a first estimated covariance value of the portion of the hyperspectral image;

[0040] Among them, the partial hyperspectral image refers to a part of a complete hyperspectral image. If the complete hyperspectral image is acquired by the hyperspectral camera by scanning one row at a time, the partial hyperspectral image may be one data row or multiple data rows, that is, the partial hyperspectral image is at least one row of data in the complete hyperspectral image created by scanning the scene one row at a time.

[0041] Optionally, the partial hyperspectral image may be acquired from a device for collecting hyperspectral images (eg, a hyperspectral camera), or acquired from a data storage space containing a complete hyperspectral image.

[0042] The calculation of the first estimated mean and the first estimated covariance value of the partial hyperspectral image may be implemented based on the existing technology, which will not be described in detail here.

[0043] S20, using the first estimated mean value and the first estimated covariance value as estimated values ​​of a global mean value and a global covariance value of a complete hyperspectral image corresponding to the partial hyperspectral image, respectively;

[0044] S30, based on the global average value and the global covariance value, the complete hyperspectral image is processed by a hyperspectral image processing algorithm to obtain a processing result.

[0045] Among them, the hyperspectral image processing algorithm includes but is not limited to solutions for direct or indirect processing of the complete hyperspectral image, such as anomaly detection algorithms, such as detecting anomalies in the complete hyperspectral image, and target detection algorithms, such as detecting specified targets in the complete hyperspectral image.

[0046] Optionally, the above method further includes:

[0047] When receiving another part of the hyperspectral image except the part of the hyperspectral image in the complete hyperspectral image, calculating a second estimated mean value and a second estimated covariance value of the other part of the hyperspectral image;

[0048] Based on the second estimated mean value and the second estimated covariance value, the global average value and the global covariance value are updated to obtain a new global average value and a new global covariance value, that is, the global average value and the global covariance value in S30 are updated based on the second estimated mean value and the second estimated covariance value. Then, based on the new global average value and the new global covariance value, the complete hyperspectral image is processed by a hyperspectral image processing algorithm.

[0049] Optionally, one implementation of updating the global average value and the global covariance value based on the second estimated mean value and the second estimated covariance value to obtain a new global average value and a new global covariance value is:

[0050] The first one is to directly use the second estimated mean as the new global mean and the second estimated covariance value as the new global covariance value;

[0051] The second method is to calculate a first mean between the first estimated mean and the second estimated mean, use the first mean as the new global mean, calculate a second mean between the first estimated covariance value and the second estimated covariance value, and use the second mean as the new global mean.

[0052] It can be understood that if the complete hyperspectral image is acquired by the hyperspectral camera by scanning row by row, and the partial hyperspectral image is row by row of data, then the other partial hyperspectral image may be at least one row of data except the row data corresponding to the partial hyperspectral image, and the above-mentioned scheme for updating the global mean value and the global covariance value is also applicable to this case.

[0053] As an example, part of the hyperspectral image is the first row of data, and the other part of the hyperspectral image is the second row of data, then the global average value and the global covariance value can be updated based on the above method. When the third row of data is obtained, the global average value and the global covariance value can be updated based on the row data of the first to third rows using the second method described above.

[0054] Optionally, if the complete hyperspectral image is acquired by a hyperspectral camera by scanning row by row, the method further includes:

[0055] If the partial hyperspectral image is the first row of data, then when the second row of data is received, the first row of data and the second row of data can be combined and processed together through the hyperspectral image processing algorithm, that is, two rows of data are processed together, and so on. In the process of continuously acquiring the row data of the complete hyperspectral image, the acquired row data can be processed in real time.

[0056] Optionally, the method further comprises:

[0057] The first relevant data of the complete hyperspectral image and / or the second relevant data of the processing result are transmitted to a remote device, wherein the first relevant data includes the partial hyperspectral image and / or the another partial hyperspectral image.

[0058] In this embodiment, only the estimated mean and estimated covariance values ​​for further processing may be stored, specifically including the first estimated mean, the second estimated mean, the first estimated covariance value, the second estimated covariance value, the global estimated covariance value and the global estimated mean, without storing a partial hyperspectral image, another partial hyperspectral image and a complete hyperspectral image.

[0059] In order to better illustrate and understand the principle of the method provided by the present invention, the scheme of the present invention is described below in conjunction with an optional specific embodiment. It should be noted that the specific implementation of each step in the specific embodiment should not be understood as a limitation to the scheme of the present invention. On the basis of the principle of the scheme provided by the present invention, other implementations that can be thought of by those skilled in the art should also be considered within the scope of protection of the present invention.

[0060] Combination Figure 2 The system corresponding to the hardware device involved in this embodiment is described. In this embodiment, Figure 2 The hyperspectral camera 101 shown in the figure can be any camera that can be used to capture hyperspectral images. In some cases, the camera can be fixed on a mobile platform so as to perform directional scanning under remote control to obtain partial hyperspectral images and complete hyperspectral images.

[0061] The hyperspectral camera 101 communicates with a computing device 104, which is configured to receive a portion of the hyperspectral image from the camera and process the portion of the hyperspectral image using the application 102. The computing device 104 can be any suitable computing device with a processor and memory (e.g., a laptop or desktop personal computer), and can communicate with other devices (e.g., cameras) using any suitable wired or wireless communication link (e.g., USB, Bluetooth, etc.).

[0062] The computing device 104 is also connected to or includes a display 103, such as an LCD monitor or any other suitable device, which can be used to display a representation of the image data and / or other information related to the results of data processing. Although these components are shown as separate blocks in the figure and can be remotely located from each other (e.g., the hyperspectral camera 101 can be located on the street, the computing device in the control center, and the display in the monitoring station), it will be understood that in some embodiments, all or some of them can be integrated into one device, such as a portable camera with onboard processing and / or display.

[0063] Figure 3 An example of the main steps performed by the application 102 executed on the computing device 104 is shown. The skilled person will understand that these steps are exemplary only, and in alternative embodiments, some of them may be omitted and "or reordered". In addition, the method may be implemented using any suitable programming language and data structure.

[0064] In step 111, the computing device 104 receives data representing a portion of a complete hyperspectral image, that is, receives a partial hyperspectral image. It is understood that the received partial hyperspectral image may be in any format, which is not limited in the present application.

[0065] Before processing a portion of the hyperspectral image, data conversion, decompression and / or decryption processing may be performed on the portion of the hyperspectral image. This process may be performed by the application 102 .

[0066] In some embodiments, the partial hyperspectral image may represent a row of data of a complete hyperspectral image created by scanning a scene one row at a time, for example, using the movement of an airborne platform or a mobile vehicle on which the hyperspectral camera 101 is mounted to scan the entire surface. A complete hyperspectral image typically includes a known number of rows of data, and the partial hyperspectral image may be data corresponding to at least one row of data.

[0067] In other embodiments, the partial hyperspectral image may represent multiple rows of data in the complete hyperspectral image, or another portion / block in the complete hyperspectral image. Figure 3 The steps in are performed in “real time” (or substantially real time) when the hyperspectral image data is received from the hyperspectral camera 101, but in other cases, a partial hyperspectral image may also be obtained from a data storage device containing data representing the complete hyperspectral image.

[0068] In step 112, the received partial hyperspectral image is processed by the application 102 to generate a first estimated mean and a first estimated covariance. The statistically based hyperspectral image processing method is a method for image processing based on the estimation of the mean and spectral covariance of the hyperspectral image. In general, the algorithm described in this article can be used, that is, the first estimated mean μ and the first estimated covariance ∑ corresponding to the partial hyperspectral image are used as estimates of the global mean and global covariance values ​​of the complete hyperspectral image. The method performed by the application 102 is based on the assumption that each row of data received represents a random sample of the complete hyperspectral image. The mean (first estimated mean) and covariance (first estimated covariance value) (μ and ∑) of the straight line are "unbiased estimates" of the global mean (global mean) and global covariance (global covariance value) of the complete hyperspectral image.

[0069]

[0070] Convariance(s) represents the “covariance of spectrum s”, that is, the first estimated covariance value; mean(s) represents the “average value of spectrum s”, that is, the first estimated mean; n represents the “number of spectra”; ∑ represents the global covariance value corresponding to the “unbiased estimate”; n` represents the global average value of the “n`th spectrum”.

[0071] The mean and covariance can be used to run one or more algorithms without having to wait for more data to be received.

[0072] In the example method, the global mean and global covariance values ​​may be used by two hyperspectral image processing algorithms 113a (object detection algorithm), 113b (anomaly detection algorithm), but it will be appreciated that the method may use any reasonable number of suitable algorithms of the method.

[0073] Step 114, displaying the processing results of the hyperspectral image processing algorithms 113a, 113b on the display 103 in any suitable form, such as a notification that a target / anomaly has been detected and detailed information about the location of the target / anomaly. It should be understood that in embodiments that implement different hyperspectral image processing algorithms, the output can be varied to provide any suitable output, such as any graphical and / or textual information, etc.

[0074] In some embodiments, when a new partial hyperspectral image is acquired (e.g., another partial hyperspectral image or a new row of data), the global mean value and the global covariance value can be updated based on the estimated mean value (for convenience of description, it can also be called the new estimated mean value) and the estimated covariance value (for convenience of description, it can also be called the new estimated covariance value) of the new partial hyperspectral image. In this case, the process is again started from Figure 3 The process starts at step 111. Optionally, the new estimated mean and the new estimated covariance may be average values ​​between the new partial hyperspectral image and the previous / existing estimated mean and estimated covariance.

[0075] Applications of the methods described herein may include using the real-time detection results to send only the important parts of the complete hyperspectral image to ground operators, and using the detection results to prompt cameras to capture detailed images of areas of interest.

[0076] Based on Figure 1 Based on the same principle as the method shown in , the embodiment of the present invention also provides a hyperspectral image processing device 20, such as Figure 4 As shown in , the hyperspectral image processing device 20 may include a first calculation module 210, a first determination module 220 and a processing module 230, wherein:

[0077] A first calculation module 210 is used to calculate a first estimated mean and a first estimated covariance value of the partial hyperspectral image when receiving the partial hyperspectral image;

[0078] A first determining module 220 is configured to use the first estimated mean value and the first estimated covariance value as estimated values ​​of a global mean value and a global covariance value of a complete hyperspectral image corresponding to the partial hyperspectral image, respectively;

[0079] The processing module 230 is used to process the complete hyperspectral image based on the global average value and the global covariance value by using a hyperspectral image processing algorithm to obtain a processing result.

[0080] Optionally, the device further comprises:

[0081] A second calculation module is used for calculating a second estimated mean and a second estimated covariance value of another part of the hyperspectral image when receiving another part of the hyperspectral image except the part of the hyperspectral image in the complete hyperspectral image;

[0082] The second determining module is used to update the global average value and the global covariance value based on the second estimated mean value and the second estimated covariance value to obtain a new global average value and a new global covariance value.

[0083] Optionally, the partial hyperspectral image is acquired from a device for collecting hyperspectral images, or is acquired from a data storage space containing a complete hyperspectral image.

[0084] Optionally, the device further comprises:

[0085] A transmission module is used to transmit first relevant data of the complete hyperspectral image and / or second relevant data of the processing result to a remote device, wherein the first relevant data includes the partial hyperspectral image and / or the other partial hyperspectral image.

[0086] Optionally, the partial hyperspectral image is at least one row of data in a complete hyperspectral image created by scanning a scene one row at a time.

[0087] The hyperspectral image processing device of the embodiment of the present invention can execute the hyperspectral image processing method provided by the embodiment of the present invention, and its implementation principle is similar. The actions performed by each module and unit in the hyperspectral image processing device in each embodiment of the present invention correspond to the steps in the hyperspectral image processing method in each embodiment of the present invention. For the detailed functional description of each module of the hyperspectral image processing device, please refer to the description in the corresponding hyperspectral image processing method shown in the previous text, which will not be repeated here.

[0088] The hyperspectral image processing device may be a computer program (including program code) running in a computer device, for example, the hyperspectral image processing device is an application software; the device may be used to execute the corresponding steps in the method provided in the embodiment of the present invention.

[0089] In some embodiments, the hyperspectral image processing device provided by the embodiment of the present invention can be implemented in a combination of software and hardware. As an example, the hyperspectral image processing device provided by the embodiment of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the hyperspectral image processing method provided by the embodiment of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application specific integrated circuits (ASIC, Application Specific Integrated Circuit), DSP, programmable logic device (PLD, Programmable Logic Device), complex programmable logic device (CPLD, Complex Programmable Logic Device), field programmable gate array (FPGA, Field-Programmable Gate Array) or other electronic components.

[0090] In other embodiments, the hyperspectral image processing device provided by the embodiments of the present invention can be implemented in a software manner. Figure 4 A hyperspectral image processing device stored in a memory is shown, which may be software in the form of a program and a plug-in, and includes a series of modules, including a first calculation module 210, a first determination module 220 and a processing module 230, for implementing the hyperspectral image processing method provided in an embodiment of the present invention.

[0091] The modules involved in the embodiments of the present invention may be implemented by software or hardware, wherein the name of a module does not limit the module itself in some cases.

[0092] Based on the same principle as the method shown in the embodiments of the present invention, an electronic device is also provided in the embodiments of the present invention, which may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0093] In an alternative embodiment, an electronic device is provided, such as Figure 5 As shown, Figure 5The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0094] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0095] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0096] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0097] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0098] The electronic device may also be a terminal device. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0099] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0100] According to another aspect of the present invention, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the implementation modes of the above-mentioned various embodiments.

[0101] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0102] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0103] The computer-readable storage medium provided by the embodiment of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0104] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0105] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

Claims

1. A hyperspectral image processing method, characterized in that: The following steps are involved: When receiving a portion of the hyperspectral image, calculating a first estimated mean value and a first estimated covariance value of the portion of the hyperspectral image; Using the first estimated mean value and the first estimated covariance value as estimated values ​​of the global mean value and the global covariance value of the complete hyperspectral image corresponding to the partial hyperspectral image, respectively; Based on the global average value and the global covariance value, the complete hyperspectral image is processed by a hyperspectral image processing algorithm to obtain a processing result.

2. The method according to claim 1, characterized in that The method further comprises: When receiving another part of the hyperspectral image except the part of the hyperspectral image in the complete hyperspectral image, calculating a second estimated mean value and a second estimated covariance value of the other part of the hyperspectral image; Based on the second estimated mean value and the second estimated covariance value, the global average value and the global covariance value are updated to obtain a new global average value and a new global covariance value.

3. The method according to claim 1, characterized in that: The partial hyperspectral image is acquired from a device for collecting the hyperspectral image, or is acquired from a data storage space containing a complete hyperspectral image.

4. The method according to claim 2 or 3, characterized in that: The method further comprises: The first relevant data of the complete hyperspectral image and / or the second relevant data of the processing result are transmitted to a remote device, wherein the first relevant data includes the partial hyperspectral image and / or the another partial hyperspectral image.

5. The method according to any one of claims 1 to 3, characterized in that The partial hyperspectral image is at least one row of data in a complete hyperspectral image created by scanning the scene one row at a time.

6. A hyperspectral image processing device, characterized in that: include: A first calculation module is used to calculate a first estimated mean value and a first estimated covariance value of the partial hyperspectral image when receiving the partial hyperspectral image; A first determining module is used to use the first estimated mean value and the first estimated covariance value as estimated values ​​of a global mean value and a global covariance value of a complete hyperspectral image corresponding to the partial hyperspectral image, respectively; The processing module is used to process the complete hyperspectral image through a hyperspectral image processing algorithm based on the global average value and the global covariance value to obtain a processing result.

7. The device according to claim 6, characterized in that The device also includes: A second calculation module is used for calculating a second estimated mean and a second estimated covariance value of another part of the hyperspectral image when receiving another part of the hyperspectral image except the part of the hyperspectral image in the complete hyperspectral image; The second determining module is used to update the global average value and the global covariance value based on the second estimated mean value and the second estimated covariance value to obtain a new global average value and a new global covariance value.

8. The device according to claim 6, characterized in that The partial hyperspectral image is acquired from a device for collecting the hyperspectral image, or is acquired from a data storage space containing a complete hyperspectral image.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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