A remote sensing image aircraft segmentation method and system based on aggregation control module

Abstract

The present invention discloses a method and system for segmenting aircraft in remote sensing images based on an aggregation control module. The method comprises: obtaining a remote sensing image including an aircraft and inputting it into a preprocessing module to obtain an input image; inputting the input image into a first convolutional layer to obtain an input feature map; inputting the input feature map into an aggregation control module including a linear layer, multiple convolutional units, and a lightweight linear layer; inputting the input features into the linear layer and the lightweight linear layer to obtain initial feature maps and weight matrices respectively; inputting the initial feature maps into the convolutional units in sequence to obtain extracted feature maps of multiple different layers; aggregating all the extracted feature maps based on the weight matrix to obtain an aggregated feature map; and inputting the aggregated feature map into a segmentation module to obtain an aircraft segmentation result of the remote sensing image. The method improves the segmentation accuracy of aircraft targets in remote sensing images under complex backgrounds.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and more particularly to a remote sensing image aircraft segmentation method and system based on an aggregation control module. Background Art

[0002] Currently, remote sensing image segmentation technology is widely used in fields such as geographic information systems (GIS), urban planning, environmental monitoring, disaster assessment, and agricultural monitoring. Remote sensing images typically feature large areas, complex backgrounds, and diverse targets. Aerial remote sensing images, in particular, often contain significant background noise, occlusions, and multi-scale targets, posing significant challenges to target segmentation. In particular, targets in remote sensing images, such as aircraft and ships, are typically small and situated against complex backgrounds. Background features such as clouds, mountains, and rivers often share similar characteristics (such as color and texture) with the target objects, easily leading to missegmentation. With the advancement of deep learning technology, convolutional neural networks (CNNs) have gradually become the mainstream for remote sensing image segmentation. Typical deep learning segmentation networks, such as fully convolutional networks (FCNs), U-Nets, and SegNets, have achieved significant progress in medical imaging, satellite imagery, and remote sensing image segmentation tasks.

[0003] However, the performance of traditional CNN models degrades when processing images with large variations in object scale, complex backgrounds, and inconsistent image resolution. For segmenting small objects like airplanes, traditional convolution operations can struggle to capture fine target features. Therefore, researchers are focusing on effectively utilizing multi-scale information, enhancing the model's contextual awareness, and improving the model's segmentation accuracy for small objects.

[0004] Therefore, how to effectively utilize multi-scale information to improve the segmentation accuracy of aircraft targets in remote sensing images is an urgent problem that technicians in this field need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a remote sensing image aircraft segmentation method and system based on an aggregation control module, which utilizes multi-scale contextual information and structural perception capabilities to improve the segmentation accuracy of aircraft targets in remote sensing images under complex backgrounds.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A remote sensing image aircraft segmentation method based on an aggregation control module, comprising:

[0008] Acquire a remote sensing image including the aircraft and input it into a preprocessing module to obtain an input image;

[0009] Inputting the input image into the first convolutional layer to obtain an input feature map;

[0010] Based on the input feature map, an aggregation control module including a linear layer, multiple convolution units and a lightweight linear layer is input;

[0011] The input features are respectively input into the linear layer and the lightweight linear layer to obtain an initial feature map and a weight matrix respectively;

[0012] The initial feature map is sequentially input into the convolution unit to obtain a plurality of extracted feature maps of different layers;

[0013] Aggregating all the extracted feature maps based on the weight matrix to obtain an aggregated feature map;

[0014] The aggregated feature map is input into a segmentation module to obtain an aircraft segmentation result of the remote sensing image.

[0015] Preferably, the pre-processing module includes: a denoising unit, an image enhancement unit, a cropping unit, a scaling unit and a normalization unit;

[0016] The remote sensing image is input to the denoising unit for denoising to obtain a denoised image;

[0017] The denoised image is input to the image enhancement unit for image enhancement to obtain an enhanced image;

[0018] The enhanced image is input to the cropping unit for cropping to obtain a cropped image;

[0019] The cropped image is input to the scaling unit for scaling to obtain a scaled image;

[0020] The scaled image is input to the normalization unit for format conversion to obtain the input image.

[0021] Preferably, the multiple convolution units are sequentially connected and have the same structure, and all include: a depth convolution layer and an activation layer;

[0022] The initial feature map is sequentially input into the depth convolution layer and the activation layer to obtain the extracted feature map of the corresponding layer.

[0023] Preferably, the method for obtaining the aggregated feature map is:

[0024] weights corresponding to the number of channels in the weight matrix consistent with the number of layers of the extracted feature map as corresponding layer perception weights;

[0025] The aggregated feature map is obtained by performing a weighted summation based on element-by-element multiplication of all the layer perception weights and the extracted feature maps corresponding to the number of layers.

[0026] Preferably, the segmentation module includes: a second convolution layer, a deconvolution layer and an activation function layer;

[0027] The aggregated feature map is input into the second convolutional layer to obtain a processed feature map;

[0028] The processed feature map is input into the deconvolution layer for upsampling to obtain an upsampled feature map;

[0029] The upsampled feature map is input into the activation function layer to obtain the classification probability of each pixel in the remote sensing image;

[0030] The aircraft segmentation result is obtained based on the classification probability.

[0031] Preferably, obtaining the aircraft segmentation result based on the classification probability specifically includes:

[0032] Determine the segmentation result at position (i, j) in the remote sensing image based on the classification probability

[0033] Among them, P ij represents the classification probability at position (i, j) in the remote sensing image, Indicates setting threshold;

[0034] Based on all the segmentation results Get the final aircraft segmentation result R seg .

[0035] Preferably, the model training process is also included:

[0036] An aircraft segmentation model is formed based on the preprocessing module, the first convolutional layer, the aggregation control module and the segmentation module;

[0037] Obtain multiple remote sensing images including aircraft and their corresponding category labels as training sets;

[0038] The aircraft segmentation model is optimized and trained based on the training set and the total loss function to obtain a trained aircraft segmentation model.

[0039] Preferably, the total loss function L total Specifically:

[0040] L total =αL ce +βL d

[0041] Among them, L ce represents the cross entropy loss function, α represents the weight coefficient of the cross entropy loss function, L dRepresents the Dice coefficient loss function, and β represents the weight coefficient of the Dice coefficient loss function.

[0042] Preferably, the cross entropy loss function L ce Specifically:

[0043]

[0044] Among them, H and W represent the height and width of the remote sensing image respectively, and N represents the number of categories. Indicates whether the remote sensing image position (i, j) belongs to category k, Represents the predicted probability of belonging to category k at the remote sensing image location (i, j);

[0045] The Dice coefficient loss function L d Specifically:

[0046]

[0047] Among them, y ij Represents the category label at the remote sensing image location (i, j).

[0048] A remote sensing image aircraft segmentation system based on an aggregation control module includes: a data processing module, a feature extraction module, a feature processing module, a multi-layer feature acquisition module, a feature map aggregation module and a segmentation result output module;

[0049] The data processing module is used to obtain a remote sensing image including an aircraft and input it into a preprocessing module to obtain an input image;

[0050] The feature extraction module is configured to input the input image into the first convolutional layer to obtain an input feature map;

[0051] The feature processing module is used to input the input feature map into an aggregation control module including a linear layer, multiple convolution units and a lightweight linear layer; the input features are respectively input into the linear layer and the lightweight linear layer to obtain an initial feature map and a weight matrix accordingly;

[0052] The multi-layer feature acquisition module is used to sequentially input the initial feature map into the convolution unit to obtain extracted feature maps of multiple different layers;

[0053] The feature map aggregation module is configured to aggregate all the extracted feature maps based on the weight matrix to obtain an aggregated feature map;

[0054] The segmentation result output module is used to input the aggregated feature map into a segmentation module to obtain an aircraft segmentation result of the remote sensing image.

[0055] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a remote sensing image aircraft segmentation method and system based on an aggregation control module, which has the following beneficial effects:

[0056] 1. Improve segmentation accuracy: The present invention introduces multi-level contextual information through the aggregation control module, so that the model can better capture the details and structural information of aircraft targets in remote sensing images, especially in complex backgrounds. It can effectively separate the target and background and reduce missegmentation. While improving the target segmentation accuracy, it enhances the ability to suppress complex backgrounds and improves the reliability of the segmentation results.

[0057] 2. By combining deep convolution with global average pooling operations, the present invention enhances the receptive field of the model, thereby improving the ability to capture a wide range of contextual information, so that small object targets in remote sensing images, such as airplanes, can be segmented more accurately.

[0058] 3. Through improved convolution and deconvolution operations, the present invention can be performed efficiently in resource-constrained environments and is applicable to various remote sensing image analysis tasks. Moreover, through efficient parameter fine-tuning and the use of adapters, the resource requirements for model training and deployment are reduced, making it applicable to remote sensing image processing tasks in various computing environments. It has great technical value in target detection and segmentation tasks of aerial remote sensing images.

[0059] 4. This invention significantly improves segmentation accuracy by constructing an aggregation control module, combining multi-level contextual information and structural perception capabilities. By weightedly fusing features at different scales, it can better adapt to segmentation tasks involving complex backgrounds and multi-scale targets. By aggregating multi-level contextual information, this method not only effectively suppresses background noise, but also improves segmentation robustness and enhances the segmentation accuracy of small objects.

[0060] 5. Efficient Computing and Resource Utilization and Good Generalization: Utilizing fine-tuning and adapter techniques, this invention significantly reduces the computational resources required for training and inference while maintaining high accuracy, making it suitable for resource-constrained environments such as embedded systems and edge computing platforms. By aggregating context at multiple scales and focal levels, the model is highly adaptable and can process remote sensing imagery of varying resolutions and environments, enhancing its generalization and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0062] Figure 1 This is a flow chart of a remote sensing image aircraft segmentation method based on an aggregation control module provided by the present invention.

[0063] Figure 2 This is a schematic diagram of the structure of the aggregation control module provided by the present invention.

[0064] Figure 3 Schematic diagram of the data processing process of the aggregation control module provided by the present invention.

[0065] Figure 4 This is a structural schematic diagram of the aircraft segmentation model provided by the present invention.

[0066] Figure 5 This is a structural schematic diagram of a remote sensing image aircraft segmentation system based on an aggregation control module provided by the present invention. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] Example 1

[0069] like Figure 1 As shown, the embodiment of the present invention discloses a remote sensing image aircraft segmentation method based on an aggregation control module, comprising:

[0070] Acquire a remote sensing image including the aircraft and input it into a preprocessing module to obtain an input image;

[0071] Based on the input image, it is input to the first convolution layer to obtain the input feature map;

[0072] Based on the input feature map, the input is fed into an aggregation control module including a linear layer, multiple convolutional units, and a lightweight linear layer;

[0073] The input features are input to the linear layer and the lightweight linear layer respectively, and the initial feature map and weight matrix are obtained accordingly;

[0074] The initial feature map is sequentially input into the convolution unit to obtain multiple extracted feature maps at different layers;

[0075] Aggregate all extracted feature maps based on the weight matrix to obtain an aggregated feature map;

[0076] Based on the aggregated feature map, it is input into the segmentation module to obtain the aircraft segmentation result of the remote sensing image.

[0077] Example 2

[0078] The embodiment of the present invention discloses a remote sensing image aircraft segmentation method based on an aggregation control module, comprising:

[0079] Acquire a remote sensing image including the aircraft and input it into a preprocessing module to obtain an input image;

[0080] Preferably, the pre-processing module includes: a denoising unit, an image enhancement unit, a cropping unit, a scaling unit and a normalization unit;

[0081] The remote sensing image is input to the denoising unit for denoising to obtain a denoised image;

[0082] The denoised image is input to the image enhancement unit for image enhancement to obtain an enhanced image;

[0083] The enhanced image is input to the cropping unit for cropping to obtain a cropped image;

[0084] The cropped image is input to the scaling unit for scaling processing to obtain a scaled image;

[0085] The scaled image is input to the normalization unit for format conversion to obtain the input image.

[0086] Preferably, remote sensing images are often affected by noise during the acquisition process, so denoising is required to improve image quality. Common denoising methods include mean filtering, median filtering, and Gaussian filtering. Assume that the original remote sensing image is I(x, y), where x and y are the spatial coordinates of the image. Each pixel value in the image may be affected by noise N(x, y), that is, the noisy remote sensing image I noisy (x,y) is:

[0087] I noisy (x,y)=I(x,y)+N(x,y).

[0088] Preferably, the purpose of denoising is to restore the original image I(x, y). In this embodiment, the denoising unit adopts Gaussian filtering to denoise. Gaussian filtering is implemented by convolution operation. The denoised image I d (x,y) is:

[0089] I d (x,y)=(Inoisy *G);

[0090] Among them, * represents the convolution operation and G represents the filter kernel.

[0091] Preferably, the image enhancement unit can improve the contrast and details of the image, especially in remote sensing images, and can highlight certain specific features. Common enhancement methods include histogram equalization and contrast stretching. In this embodiment, the image enhancement unit uses contrast stretching to enhance the denoised image:

[0092]

[0093] Among them, I en (x,y) represents the enhanced image, represents the minimum pixel value in the denoised image, represents the maximum pixel value in the denoised image, C max Indicates the maximum value of the enhanced pixel value range, C min Indicates the minimum value of the enhanced pixel value range.

[0094] Preferably, in order to reduce the amount of calculation and focus on the target area, the enhanced image is sequentially input to the cropping unit and the scaling unit for processing. The cropping unit selects the cropping area of ​​the image: (x min ,y min ,x max ,y max ), where x min Indicates the minimum horizontal coordinate of the clipping area (i.e. the horizontal coordinate of the left edge of the clipping area), y min Indicates the minimum ordinate of the clipping area (i.e. the ordinate of the upper boundary of the clipping area), x max Indicates the maximum horizontal coordinate of the clipping area (i.e. the horizontal coordinate of the right edge of the clipping area), y max represents the maximum ordinate of the cropping region (i.e., the ordinate of the lower boundary of the cropping region); the enhanced image is cropped based on the cropping region to obtain the cropped image I crop (x,y):

[0095] I crop (x,y)=I en (x,y)for x min ≤x≤x max ,y min ≤y≤y max .

[0096] Preferably, the cropped image I crop (x, y) is input to the scaling unit, and the target size is (W, H), then the scaled image I scaled (x',y') can be expressed as:

[0097] I scaled (x',y')=I crop (x,y)where(x',y')=scale(x,y,W,H)

[0098] Among them, scale represents the scale function, which is used to crop the image I crop The coordinates (x,y) in (x,y) are scaled to the dimensions (W,H) of the destination image.

[0099] Preferably, the scaled image is input to the normalization unit for format conversion, and converted into a format suitable for model input to obtain an input image. In this embodiment, the normalization unit converts the image into an input image of size C×H×W. Where C represents the number of channels (usually 3 for color images), and H and W are the height and width of the scaled image, respectively.

[0100] Preferably, standardized image data for input into the model can be obtained by performing a series of preprocessing operations on the remote sensing images, thereby ensuring data consistency and efficiency.

[0101] Based on the input image, it is input to the first convolutional layer to obtain the input feature map.

[0102] like Figure 2 As shown, based on the input feature map, it is input to an aggregation control module including a linear layer, multiple convolutional units and a lightweight linear layer.

[0103] The input features are input to the linear layer and the lightweight linear layer respectively, and the initial feature map and weight matrix are obtained accordingly.

[0104] Preferably, the input feature map X is projected into the new feature space through a linear layer to obtain the initial feature map f proj Represents a linear projection operation.

[0105] Preferably, a linear projection operation is performed on the input feature map X and the extracted feature maps of each layer obtained by subsequent processing based on the fully connected layer in the lightweight linear layer to obtain a weight matrix: Where Φ represents the fully connected layer.

[0106] The initial feature maps are sequentially input into the convolutional units to obtain extracted feature maps of multiple different layers.

[0107] Preferably, the multiple convolution units are sequentially connected and have the same structure, and all include: a depth convolution layer and an activation layer;

[0108] The initial feature map is sequentially input into the depth convolution layer and activation layer to obtain the extracted feature map of the corresponding layer.

[0109] Preferably, in this embodiment, the activation layer adopts GeLu activation function.

[0110] Preferably, a hierarchical context representation is obtained by stacking L convolutional units in series, and the l-th layer extracted feature map Z obtained by the l-th convolutional unit l for:

[0111] Z l =GeLU(DWConv(Z l-1 ));

[0112] l∈{1,...,L};

[0113] Among them, DWConv represents the depth convolution operation, GeLu represents the activation function,

[0114] Preferably, the convolution kernel size of the depth convolution layer in this embodiment is k l Compared to pooling operations, depthwise convolution is learnable and structure-aware. Unlike regular convolution, depthwise convolution operates on a channel-by-channel basis, resulting in lower computational overhead.

[0115] At layer l, the effective receptive field of the deep convolutional layer is Its effective receptive field size is much larger than the convolution kernel size k l To capture the global context of the entire input, especially high-resolution input, a global average pooling operation is applied, which jointly captures both short-term and long-term context at different levels of granularity.

[0116] like Figure 3 As shown, all the extracted feature maps are aggregated based on the weight matrix to obtain the aggregated feature map;

[0117] Preferably, the method for obtaining the aggregated feature map is:

[0118] The weights corresponding to the number of channels in the weight matrix that are consistent with the number of layers of the extracted feature map are used as the corresponding layer perception weights;

[0119] The aggregated feature map is obtained by performing element-by-element weighted summation based on the perception weights of all layers and the extracted feature maps of the corresponding number of layers.

[0120] Preferably, based on the number of channels corresponding to the number of extracted feature layers in the weight matrix ▽, each channel has a corresponding weight, and the weight corresponding to the number of channels l in the weight matrix ▽ that is consistent with the number of layers l of the extracted feature map is used as the corresponding layer perception weight ▽ l .

[0121] Preferably, the aggregate feature map is Z out :

[0122]

[0123] Preferably, for markers located on small objects, the module focuses more on the fine-grained local structure of the low-focus level; while for markers located in a uniform background, it needs to pay attention to the wider context from a higher level. So far, all aggregation operations are spatial-level aggregation.

[0124] Based on the aggregated feature map, it is input into the segmentation module to obtain the aircraft segmentation result of the remote sensing image.

[0125] Preferably, the segmentation module includes: a second convolution layer, a deconvolution layer and an activation function layer;

[0126] The aggregated feature map is input to the second convolutional layer to obtain the processed feature map;

[0127] The processed feature map is input to the deconvolution layer for upsampling to obtain the upsampled feature map;

[0128] The upsampled feature map is input into the activation function layer to obtain the classification probability of each pixel in the remote sensing image;

[0129] The aircraft segmentation result is obtained based on the classification probability.

[0130] Preferably, the aggregate feature map Input to the second convolutional layer, the processed feature map Z′=Conv(Z out ), where Conv represents the convolution operation. Z' is a feature map of size H × W × C', where C' is the number of channels after convolution.

[0131] Preferably, in this embodiment, the convolution kernel size of the second convolutional layer is K×K, and the stride is 1.

[0132] Preferably, deconvolution can gradually enlarge the size of the processed feature map Z', and finally obtain an upsampled feature map with the same size as the original input image: in, C out Indicates the number of output channels, usually for multi-category segmentation, C out is equal to the number of categories N, and for the two-category problem, C out =1.

[0133] Preferably, the upsampled feature map is input to the activation function layer for processing to obtain the classification probability of each pixel. For multi-category segmentation, the activation function layer of this embodiment adopts the Softmax function:

[0134]

[0135] in, Represents the value of the kth class at position (i, j) in the upsampled feature map, and N represents the number of categories.

[0136] Preferably, obtaining the aircraft segmentation result based on the classification probability specifically includes:

[0137] Determine the segmentation result at position (i, j) in the remote sensing image based on the classification probability

[0138]

[0139] Among them, P ij represents the classification probability at position (i, j) in the remote sensing image, Indicates setting threshold;

[0140] Based on all segmentation results Get the final aircraft segmentation result R seg .

[0141] Preferably, in the embodiment, a threshold is set is 0.5.

[0142] Preferably, through the above steps, the aircraft target in the remote sensing image can be accurately segmented, and the model can not only identify the boundary of the aircraft target, but also handle complex backgrounds and multi-scale targets, thereby providing efficient remote sensing image segmentation results.

[0143] Preferably, the model training process is also included:

[0144] like Figure 4 As shown in the figure, the aircraft segmentation model is composed of the preprocessing module, the first convolutional layer, the aggregation control module and the segmentation module;

[0145] Obtain multiple remote sensing images including aircraft and their corresponding category labels as training sets;

[0146] The aircraft segmentation model is optimized and trained based on the training set and the total loss function to obtain a trained aircraft segmentation model.

[0147] Preferably, each sample in the training set consists of an input original image I and a corresponding category label Y, where I is the input image and Y is the true label of the image (i.e., the category label at the pixel level).

[0148] Preferably, for multi-class segmentation tasks, cross entropy loss is one of the most commonly used loss functions. Cross entropy loss measures the difference between the class probability distribution output by the model and the true label distribution. The predicted value of the aircraft segmentation model is P, which represents the predicted probability of each pixel in the i-th image belonging to each class, and the true label is Y, where the label y of each pixel is ij∈{0, 1, ..., N-1} indicates that the pixel (i, j) belongs to a certain category (N is the number of categories). The cross entropy loss function is:

[0149]

[0150] Among them, H and W represent the height and width of the remote sensing image respectively, and N represents the number of categories. Indicates whether the remote sensing image position (i, j) belongs to category k, Represents the predicted probability that the remote sensing image location (i, j) belongs to category k.

[0151] Preferably, the Dice coefficient is used to measure the similarity between two samples, and the Dice coefficient loss function can be used to optimize the segmentation accuracy of the target area. d Specifically:

[0152]

[0153] Among them, y ij Represents the category label at the remote sensing image location (i, j).

[0154] Preferably, the present invention uses cross entropy loss and Dice loss as the total loss function:

[0155] L total =αL ce +βL d

[0156] Among them, L ce represents the cross entropy loss function, α represents the weight coefficient of the cross entropy loss function, L d α represents the Dice coefficient loss function, and β represents the weight coefficient of the Dice coefficient loss function. α and β can be adjusted according to actual needs. This weighting method can take into account the impact of different loss functions on training results.

[0157] Example 3

[0158] like Figure 5 As shown, a remote sensing image aircraft segmentation system based on an aggregation control module includes: a data processing module, a feature extraction module, a feature processing module, a multi-layer feature acquisition module, a feature map aggregation module and a segmentation result output module;

[0159] A data processing module is used to obtain a remote sensing image including an aircraft and input it into a preprocessing module to obtain an input image;

[0160] A feature extraction module is used to obtain an input feature map based on the input image input to the first convolutional layer;

[0161] A feature processing module is used to input the input feature map into an aggregation control module including a linear layer, multiple convolutional units, and a lightweight linear layer; the input features are input into the linear layer and the lightweight linear layer respectively, and the initial feature map and weight matrix are obtained accordingly;

[0162] Multi-layer feature acquisition module, which is used to input the initial feature map into the convolution unit in sequence to obtain multiple extracted feature maps of different layers;

[0163] A feature map aggregation module is used to aggregate all extracted feature maps based on a weight matrix to obtain an aggregated feature map;

[0164] The segmentation result output module is used to input the segmentation module based on the aggregated feature map to obtain the aircraft segmentation result of the remote sensing image.

[0165] Preferably, the functions implemented by each module in this embodiment correspond one-to-one to the above method, and will not be described in detail here.

[0166] Example 4

[0167] Based on the same inventive concept, the present invention further provides a computer device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0168] Memory for storing computer programs;

[0169] The processor, when used to execute the program stored in the memory, can implement a remote sensing image aircraft segmentation method based on an aggregation control module as in Example 1 or 2.

[0170] The electronic device may include: a processor, a communications interface, a memory, and a communications bus, wherein the processor, the communications interface, and the memory communicate with each other via the communications bus. The processor may invoke logic instructions in the memory to execute a remote sensing image aircraft segmentation method based on an aggregation control module according to Embodiment 1 or 2.

[0171] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0172] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a remote sensing image aircraft segmentation method and system based on an aggregation control module, which has the following beneficial effects:

[0173] 1. Improve segmentation accuracy: The present invention introduces multi-level contextual information through the aggregation control module, so that the model can better capture the details and structural information of aircraft targets in remote sensing images, especially in complex backgrounds. It can effectively separate the target and background and reduce missegmentation. While improving the target segmentation accuracy, it enhances the ability to suppress complex backgrounds and improves the reliability of the segmentation results.

[0174] 2. By combining deep convolution with global average pooling operations, the present invention enhances the receptive field of the model, thereby improving the ability to capture a wide range of contextual information, so that small object targets in remote sensing images, such as airplanes, can be segmented more accurately.

[0175] 3. Through improved convolution and deconvolution operations, the present invention can be performed efficiently in resource-constrained environments and is applicable to various remote sensing image analysis tasks. Moreover, through efficient parameter fine-tuning and the use of adapters, the resource requirements for model training and deployment are reduced, making it applicable to remote sensing image processing tasks in various computing environments. It has great technical value in target detection and segmentation tasks of aerial remote sensing images.

[0176] 4. This invention significantly improves segmentation accuracy by constructing an aggregation control module, combining multi-level contextual information and structural perception capabilities. By weightedly fusing features at different scales, it can better adapt to segmentation tasks involving complex backgrounds and multi-scale targets. By aggregating multi-level contextual information, this method not only effectively suppresses background noise, but also improves segmentation robustness and enhances the segmentation accuracy of small objects.

[0177] 5. Efficient Computing and Resource Utilization and Good Generalization: Utilizing fine-tuning and adapter techniques, this invention significantly reduces the computational resources required for training and inference while maintaining high accuracy, making it suitable for resource-constrained environments such as embedded systems and edge computing platforms. By aggregating context at multiple scales and focal levels, the model is highly adaptable and can process remote sensing imagery of varying resolutions and environments, enhancing its generalization and robustness.

[0178] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0179] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A remote sensing image aircraft segmentation method based on an aggregation control module, characterized in that: include: Acquire a remote sensing image including the aircraft and input it into a preprocessing module to obtain an input image; Inputting the input image into the first convolutional layer to obtain an input feature map; Based on the input feature map, an aggregation control module including a linear layer, multiple convolution units and a lightweight linear layer is input; The input features are respectively input into the linear layer and the lightweight linear layer to obtain an initial feature map and a weight matrix respectively; Based on the fully connected layer in the lightweight linear layer, a linear projection operation is performed on the input feature map and the feature maps extracted from each layer obtained by subsequent processing to obtain a weight matrix; The initial feature map is sequentially input into the convolution unit to obtain a plurality of extracted feature maps of different layers; The multiple convolution units are sequentially connected and have the same structure, and all include: a depth convolution layer and an activation layer; The initial feature map is sequentially input into the depth convolution layer and the activation layer to obtain the extracted feature map of the corresponding layer; Aggregating all the extracted feature maps based on the weight matrix to obtain an aggregated feature map; The method for obtaining the aggregated feature map is: weights corresponding to the number of channels in the weight matrix consistent with the number of layers of the extracted feature map as corresponding layer perception weights; The aggregated feature map is obtained by performing a weighted summation based on element-by-element multiplication of all the layer perception weights and the extracted feature maps corresponding to the number of layers; The aggregated feature map is input into a segmentation module to obtain an aircraft segmentation result of the remote sensing image.

2. The method for aircraft segmentation in remote sensing images based on an aggregation control module according to claim 1, characterized in that: The preprocessing module includes: a denoising unit, an image enhancement unit, a cropping unit, a scaling unit and a normalization unit; The remote sensing image is input to the denoising unit for denoising to obtain a denoised image; The denoised image is input to the image enhancement unit for image enhancement to obtain an enhanced image; The enhanced image is input to the cropping unit for cropping to obtain a cropped image; The cropped image is input to the scaling unit for scaling to obtain a scaled image; The scaled image is input to the normalization unit for format conversion to obtain the input image.

3. The method for aircraft segmentation in remote sensing images based on an aggregation control module according to claim 1, characterized in that: The segmentation module includes: a second convolution layer, a deconvolution layer and an activation function layer; The aggregated feature map is input into the second convolutional layer to obtain a processed feature map; The processed feature map is input into the deconvolution layer for upsampling to obtain an upsampled feature map; The upsampled feature map is input into the activation function layer to obtain the classification probability of each pixel in the remote sensing image; The aircraft segmentation result is obtained based on the classification probability.

4. The method for aircraft segmentation in remote sensing images based on an aggregation control module according to claim 3, characterized in that: Obtaining the aircraft segmentation result based on the classification probability specifically includes: Determine the segmentation result at position (i, j) in the remote sensing image based on the classification probability Among them, P ij represents the classification probability at position (i, j) in the remote sensing image, Indicates setting threshold; Based on all the segmentation results Get the final aircraft segmentation result R seg .

5. The method for aircraft segmentation in remote sensing images based on an aggregation control module according to claim 4, characterized in that: It also includes the model training process: An aircraft segmentation model is formed based on the preprocessing module, the first convolutional layer, the aggregation control module and the segmentation module; Obtain multiple remote sensing images including aircraft and their corresponding category labels as training sets; The aircraft segmentation model is optimized and trained based on the training set and the total loss function to obtain a trained aircraft segmentation model.

6. The method for aircraft segmentation in remote sensing images based on an aggregation control module according to claim 5, characterized in that: The total loss function L total Specifically: L total =αL ce +βL d Among them, L ce represents the cross entropy loss function, α represents the weight coefficient of the cross entropy loss function, L d Represents the Dice coefficient loss function, and β represents the weight coefficient of the Dice coefficient loss function.

7. The method for aircraft segmentation in remote sensing images based on an aggregation control module according to claim 6, characterized in that: The cross entropy loss function L ce Specifically: Among them, H and W represent the height and width of the remote sensing image respectively, and N represents the number of categories. Indicates whether the remote sensing image position (i, j) belongs to category k, Represents the predicted probability of belonging to category k at the remote sensing image location (i, j); The Dice coefficient loss function L d Specifically: Among them, y ij Represents the category label at the remote sensing image location (i, j).

8. A remote sensing image aircraft segmentation system based on an aggregation control module, applied to a remote sensing image aircraft segmentation method based on an aggregation control module according to any one of claims 1 to 7, characterized in that: include: Data processing module, feature extraction module, feature processing module, multi-layer feature acquisition module, feature map aggregation module and segmentation result output module; The data processing module is used to obtain a remote sensing image including an aircraft and input it into a preprocessing module to obtain an input image; The feature extraction module is configured to input the input image into the first convolutional layer to obtain an input feature map; The feature processing module is used to input the input feature map into an aggregation control module including a linear layer, multiple convolution units and a lightweight linear layer; the input features are respectively input into the linear layer and the lightweight linear layer to obtain an initial feature map and a weight matrix accordingly; The multi-layer feature acquisition module is used to sequentially input the initial feature map into the convolution unit to obtain extracted feature maps of multiple different layers; The feature map aggregation module is used to aggregate all the extracted feature maps based on the weight matrix to obtain an aggregated feature map; The segmentation result output module is used to input the aggregated feature map into a segmentation module to obtain an aircraft segmentation result of the remote sensing image.

Images