Hyperspectral image travertine extraction method based on deep learning
By preprocessing and extracting features from hyperspectral images based on deep learning methods and training them using models such as convolutional neural networks, the limitations of traditional methods in accuracy and efficiency are overcome, and high-precision travertine extraction and rapid processing of big data are achieved.
Patent Information
- Application Number
- CN202510468378.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-15
- Publication Date
- 2025-09-16
AI Technical Summary
Traditional travertine extraction methods based on hyperspectral images have limitations in accuracy and efficiency, and it is difficult to effectively utilize the spectral and spatial information of images.
A deep learning-based method is used to preprocess hyperspectral images, extract spectral and spatial features, construct comprehensive feature vectors, and train models such as convolutional neural networks to achieve high-precision extraction of travertine.
It improves the accuracy and robustness of travertine extraction, has a high degree of automation, can quickly process large amounts of data, saves manpower and time costs, has strong adaptability, and is suitable for hyperspectral imaging data of different regions and types.
Smart Images
Figure CN120656053A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of hyperspectral image processing and analysis, and specifically to a hyperspectral image travertine extraction method based on deep learning. Background Art
[0002] Travertine is a significant geological phenomenon, holding significant value in geological research, environmental protection, and water resource management. Hyperspectral imagery, with its high spectral resolution and rich spectral information, provides a powerful tool for travertine identification. However, travertine spectral characteristics are complex and affected by factors such as environmental noise and image quality. Traditional extraction methods are limited in accuracy and efficiency. Therefore, developing a deep learning-based travertine extraction method from hyperspectral images is crucial for improving both accuracy and efficiency. Summary of the Invention
[0003] This invention aims to provide a deep learning-based hyperspectral imagery travertine extraction method that fully utilizes the spectral and spatial information of hyperspectral images to achieve high-precision travertine extraction. To solve the above technical problems, the present invention provides the following technical solutions:
[0004] A method for extracting travertine from hyperspectral images based on deep learning, comprising the following steps:
[0005] S1. Preprocess the collected hyperspectral images to improve the quality and accuracy of the images;
[0006] S2. Analyze the spectral characteristics of the hyperspectral image, extract characteristic information related to travertine, and construct a comprehensive feature vector based on the spatial characteristics of the image;
[0007] S3. Build a deep learning-based travertine extraction model, train the model using known travertine sample data, and optimize model performance by adjusting model parameters and hyperparameters to improve the accuracy and robustness of travertine extraction.
[0008] S4. Applying the trained deep learning model to the hyperspectral image to be extracted, classifying each pixel in the image to determine whether it belongs to the travertine category, thereby obtaining a travertine distribution map;
[0009] S5. Verify and evaluate the extraction results through field investigations, laboratory analysis, etc., and further optimize and improve the model and extraction method based on the evaluation results.
[0010] As an improvement, the preprocessing method includes radiation correction, atmospheric correction, and geometric correction.
[0011] As an improvement, the feature information in step S2 includes but is not limited to: reflectivity of a specific band and the shape of a spectral curve; the spatial features of the image include but are not limited to texture and shape.
[0012] As an improvement, the deep learning model is a convolutional neural network (CNN), a recurrent neural network (RNN) or its variants, such as a long short-term memory network (LSTM), a bidirectional LSTM (Bi-LSTM), etc., or a hybrid neural network model, such as a model combining 3-DCNN, 2-D CNN and Bi-LSTM.
[0013] As an improvement, in the model construction and training steps, the model is optimized by adjusting hyperparameters, optimizing algorithm selection, parameter initialization and other methods to improve the accuracy of travertine extraction.
[0014] As an improvement, in the data preprocessing step, the hyperspectral image is denoised and normalized to improve the training stability and convergence speed of the model.
[0015] As an improvement, in the feature extraction step, the spectral data is subjected to dimension reduction processing by methods such as principal component analysis (PCA) and independent component analysis (ICA) to extract the main spectral features, and spatial feature extraction methods such as texture analysis and edge detection are combined to construct a more comprehensive feature vector.
[0016] The advantages of the present invention are:
[0017] The present invention utilizes a deep learning model to fully utilize the spectral and spatial information of hyperspectral imagery, achieving high-precision travertine extraction and improving identification accuracy and reliability. The method disclosed in the present invention has a high degree of automation and can rapidly process large amounts of hyperspectral image data, saving manpower and time costs and improving work efficiency. The method of the present invention is highly adaptable and can be applied to hyperspectral image data from different regions and types, meeting the requirements for travertine extraction in diverse geological environments and research needs.
[0018] The present invention can be widely used in geological research, environmental protection, water resources management and other fields, providing strong technical support and data guarantee for research and practice in related fields. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 It is the overall flow chart of the present invention.
[0020] Figure 2 This is a feature extraction flow chart of the present invention.
[0021] Figure 3 Flowchart of the classification and post-processing steps of the present invention.
[0022] Figure 4Flowchart of the verification and optimization steps of the present invention. DETAILED DESCRIPTION
[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.
[0024] Example 1
[0025] This embodiment discloses a method for extracting travertine from hyperspectral images based on deep learning.
[0026] This example uses a publicly available hyperspectral image dataset as its data source. First, the acquired hyperspectral imagery undergoes preprocessing, including radiometric, atmospheric, and geometric corrections. This process eliminates sensor response variations, atmospheric absorption, and scattering, ensuring geometric accuracy and improving image quality and accuracy.
[0027] During the feature extraction phase, the spectral characteristics of the hyperspectral image are analyzed to extract characteristic information related to travertine, such as the reflectance in specific bands and the shape of the spectral curve. Furthermore, a comprehensive feature vector is constructed by combining the spatial characteristics of the image, such as texture and shape. For example, texture features are extracted by calculating the gray-level co-occurrence matrix, while shape features are extracted using edge detection algorithms.
[0028] Next, we constructed a deep learning-based travertine extraction model. In this example, we chose a convolutional neural network (CNN) as the model architecture. We trained the model using known travertine sample data. During the training process, we optimized the model's performance by adjusting hyperparameters such as the learning rate and regularization parameter, thereby improving the accuracy and robustness of travertine extraction.
[0029] After model training, the trained CNN model is applied to the hyperspectral image to be extracted. Each pixel in the image is classified as travertine, thereby obtaining a travertine distribution map. During the classification process, post-processing methods such as morphological operations are combined to further optimize the extraction results and remove noise and small areas of misclassification.
[0030] Finally, the extraction results were verified by comparing them with field survey data, and metrics such as precision, recall, and F1-score were calculated to evaluate the extraction performance. Based on these results, the model's shortcomings were analyzed, and the model structure and training strategy were adjusted accordingly to further improve the accuracy of travertine extraction.
[0031] Example 2
[0032] In this embodiment, self-collected hyperspectral image data is used as the data source. First, the collected hyperspectral image is preprocessed, including radiation correction, atmospheric correction, and geometric correction operations to improve the quality and accuracy of the image.
[0033] During the feature extraction phase, the spectral characteristics of the hyperspectral image are analyzed to extract characteristic information related to travertine, such as the reflectance of specific bands and the shape of the spectral curve. At the same time, a comprehensive feature vector is constructed by combining the spatial characteristics of the image, such as texture and shape.
[0034] This example uses a hybrid neural network model, combining 3D CNN, 2D CNN, and Bi-LSTM, to build a deep learning-based travertine extraction model. The model is trained using self-collected travertine sample data. Cross-validation and other methods are used to evaluate model performance, adjust model parameters and hyperparameters, and optimize performance.
[0035] The trained hybrid neural network model is applied to the hyperspectral image to be extracted, classifying each pixel and generating a travertine distribution map. During the classification process, post-processing methods are combined to further optimize the extraction results.
[0036] The extraction results are verified by comparing with laboratory analysis results, and the feature extraction method and model training process are optimized based on the verification results to improve the accuracy and robustness of travertine extraction.
[0037] Example 3
[0038] This embodiment adopts a multi-source hyperspectral image data fusion method. First, the images from each data source are pre-processed by radiation correction, atmospheric correction, geometric correction, etc., and then data fusion is performed to improve the richness and representativeness of the data.
[0039] During the feature extraction phase, the spectral and spatial features of the fused data are extracted to construct a more comprehensive feature vector. For example, methods such as principal component analysis (PCA) are used to reduce the dimensionality of the spectral data and extract the main spectral features. Simultaneously, image processing techniques are used to extract spatial features of the image, such as texture and shape.
[0040] When building a deep learning-based travertine extraction model, this example uses a Transformer model and introduces an attention mechanism to improve the model's ability to capture travertine features. The model is trained using the fused travertine sample data, optimizing model parameters and improving its travertine recognition capabilities.
[0041] After model training is complete, the trained Transformer model is applied to the hyperspectral image to be extracted, classifying each pixel in the image to obtain a travertine distribution map. During the classification process, post-processing methods are combined to further optimize the extraction results.
[0042] The accuracy of the extraction results was verified through field investigations and multi-source data comparisons, and targeted improvements were made to the model and extraction method based on the verification feedback to enhance the adaptability and reliability of the travertine extraction method.
[0043] In summary, the present invention provides a hyperspectral imaging travertine extraction method based on deep learning. Through specific examples, it demonstrates its implementation process and effects under different data sources and application scenarios. It has significant technical advantages and application value, can effectively solve the problems and shortcomings of existing travertine extraction methods, and promote technological progress and development in related fields.
[0044] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.
Claims
1. A method for extracting travertine from hyperspectral images based on deep learning, characterized in that: The following steps are involved: S1. Preprocess the collected hyperspectral images to improve the quality and accuracy of the images; S2. Analyze the spectral characteristics of the hyperspectral image, extract characteristic information related to travertine, and construct a comprehensive feature vector based on the spatial characteristics of the image; S3. Build a deep learning-based travertine extraction model, train the model using known travertine sample data, and optimize model performance by adjusting model parameters and hyperparameters to improve the accuracy and robustness of travertine extraction. S4. Applying the trained deep learning model to the hyperspectral image to be extracted, classifying each pixel in the image to determine whether it belongs to the travertine category, thereby obtaining a travertine distribution map; S5. Verify and evaluate the extraction results through field investigations, laboratory analysis, etc., and further optimize and improve the model and extraction method based on the evaluation results.
2. The method for extracting travertine from hyperspectral images based on deep learning according to claim 1, characterized in that: The preprocessing method includes radiation correction, atmospheric correction, and geometric correction.
3. The method for extracting travertine from hyperspectral images based on deep learning according to claim 1, characterized in that: The characteristic information in step S2 includes but is not limited to: reflectivity of a specific band and the shape of a spectrum curve; the spatial characteristics of the image include but are not limited to texture and shape.
4. The method for extracting travertine from hyperspectral images based on deep learning according to claim 1, characterized in that: The deep learning model is a convolutional neural network (CNN), a recurrent neural network (RNN) or a variant thereof, such as a long short-term memory network (LSTM), a bidirectional LSTM (Bi-LSTM), etc., or a hybrid neural network model, such as a model combining 3-D CNN, 2-D CNN and Bi-LSTM.
5. The method for extracting travertine from hyperspectral images based on deep learning according to claim 1, characterized in that: In the model construction and training steps, the model is optimized by adjusting hyperparameters, optimizing algorithm selection, parameter initialization and other methods to improve the accuracy of travertine extraction.
6. The method for extracting travertine from hyperspectral images based on deep learning according to claim 1, characterized in that: In the data preprocessing step, the hyperspectral image is denoised and normalized to improve the training stability and convergence speed of the model.
7. The method for extracting travertine from hyperspectral images based on deep learning according to claim 1, characterized in that: In the feature extraction step, the spectral data is subjected to dimension reduction processing by methods such as principal component analysis (PCA) and independent component analysis (ICA) to extract the main spectral features, and a more comprehensive feature vector is constructed by combining spatial feature extraction methods such as texture analysis and edge detection.