64 results about "Spectral space" patented technology

The invention discloses a spectrum-space depth fusion hyperspectral image classification method oriented to a small sample condition, and the method comprises the steps: firstly carrying out the multi-scale hole convolution processing of input hyperspectral data through a range attention convolution SAC module, and extracting the multi-scale context features; then, a spatial normalization attention SNA mechanism is utilized to carry out adaptive weighting adjustment of spatial dimensions on the feature map, and spatial feature representation of the key area is enhanced; the method comprises the following steps: constructing a lightweight hybrid expert model LMOE, carrying out parallel processing and gating weighting through a multi-path expert network, carrying out efficient refining and mapping on features, finally fusing processed spectral features and spatial features, and carrying out pixel-level prediction through a classifier to obtain a terrain classification result map of a hyperspectral image. The method solves the problems that in the prior art, overfitting is prone to occurring under the small sample condition, the spectrum-space collaborative modeling capacity is insufficient, the long-range dependence obtaining efficiency is low, and the recognition precision is reduced under the class imbalance scene.

The invention discloses an ultra-high-speed imaging device based on acousto-optic filtering modulation. The ultra-high-speed imaging device comprises a spectrum modulation system, a spectrum imaging system and a data processing system. The spectrum modulation system utilizes an acousto-optic tunable filter (AOTF) to dynamically modulate broadband continuous spectrum light, narrow-band illumination light which rapidly changes along with time is generated, and time information of a dynamic scene is effectively mapped to a spectrum dimension. And the spectral imaging system images the spectral coded dynamic scene to the hyperspectral camera to realize acquisition of spectral space-time information. The data processing system performs spectral crosstalk correction, channel separation and demosaicing reconstruction on the acquired original mosaic image, and recovers a time sequence image according to a spectrum-time mapping relation, thereby realizing single-shot superspeed imaging in a dynamic process. The device has the advantages of being simple and compact in structure, flexible in time window, high in imaging fidelity and the like, and has wide application prospects in the fields of rapid dynamics such as microfluidics, laser processing and ultrafast physics.

The invention relates to a geological disaster early warning algorithm based on hyperspectral and Internet of Things data fusion analysis, and relates to the technical field of geological disaster monitoring and early warning, the geological disaster early warning algorithm comprises the following steps: S1, preprocessing hyperspectral data and Internet of Things data, and respectively extracting spectral-spatial features and dynamic topology time sequence features; s2, fusing the spectrum-space features and the time sequence features to generate cross-modal joint features; s3, performing parameter optimization on the fusion features through a quantum-classical hybrid optimization algorithm, and calculating a disaster risk probability; and S4, based on the optimization result and the risk probability, executing an edge-cloud collaborative early warning decision. According to the method, through non-negative tensor ring decomposition of the hyperspectral data and dynamic topology modeling of the Internet of Things, the limitation of a traditional single data source in temporal-spatial resolution and physical relevance is solved, multi-dimensional joint extraction of spectrum-space-mechanical characteristics can be realized, and the characterization precision of a rock-soil body deformation evolution law is remarkably enhanced.

The invention discloses an automobile wire harness detection system based on artificial intelligence and machine vision, and relates to the technical field of intelligent detection, and the system comprises a feature extraction module which carries out the noise reduction and feature enhancement of hyperspectral reflection data through a low-rank sparse decomposition method, extracts the principal component features and local sparse features of an automobile wire harness sheath, and carries out the recognition of the local sparse features of the automobile wire harness sheath; spectral features are formed; the material identification module is used for identifying the category of the automobile wire harness sheath material according to the similarity distribution of the spectral characteristics in the multi-dimensional spectral space; the state analysis module is used for combining the spectral characteristics with the types of the automobile wire harness sheath materials, identifying the surface state of the automobile wire harness sheath and carrying out quantitative analysis to obtain an aging grade evaluation result; and the quality evaluation module is used for carrying out comprehensive quality evaluation on the automobile wire harness sheath according to the automobile wire harness sheath material category and the aging grade evaluation result, and generating a detection report. According to the invention, the capability of distinguishing aging modes of different materials is remarkably improved.

The invention relates to a wetland vegetation extraction method and system based on spatial-temporal feature fusion, and belongs to the field of wetland remote sensing intelligent interpretation. The problems that an existing wetland vegetation classification method does not make full use of remote sensing data time sequence features, and the wetland vegetation extraction precision is insufficient are solved. According to the technical scheme, a single optical multi-temporal remote sensing image is used as input, frequency domain decomposition and convolution long and short time memory network modeling long-term phenology and short-term fluctuation are carried out through a time-frequency enhancement time sequence branch, and spectral grouping of a spectral space branch and cavity convolution multi-scale feature extraction are combined; and a cross-domain cross attention fusion module is utilized to realize time sequence spectral space three-dimensional feature deep fusion, and finally a wetland vegetation community classification map is output. On the premise of not depending on multi-source data, the time-frequency enhanced double-branch space-time model is innovatively provided, the multi-temporal remote sensing data time sequence feature self-adaptive extraction capacity can be effectively improved, the classification stability and robustness are improved, and finally the remote sensing monitoring precision of a wetland ecosystem is improved.

The invention relates to the technical field of mine truck obstacle detection, and discloses an obstacle laser detection method and system for an unmanned mine truck, and the method comprises the steps: obtaining the signal-to-noise ratio of all detection echo signals, determining the adjustment strategy of the corresponding detection echo signals based on the signal-to-noise ratio, and determining the adjustment strategy of the corresponding detection echo signals; the method comprises the steps of constructing a point cloud topology network based on point cloud data, performing clustering to determine a spatial feature cluster, extracting spectrum features containing spectrum data based on the spatial feature cluster, determining a spatial spectrum feature cluster based on synchronous change of the spectrum features, determining an effective echo spectrum according to a spectrum space and spectrum time of the spatial spectrum feature cluster, and outputting the effective echo spectrum. And comparing the effective echo frequency spectrum with a historical frequency spectrum database to determine whether obstacle information of the effective echo frequency spectrum exists, determining the obstacle information based on an obstacle identification model and the effective echo frequency spectrum, and determining an effective obstacle of the driving route according to the obstacle information. According to the invention, the reliability and the recognition stability of obstacle detection are ensured.

The invention discloses a hyperspectral image target classification method based on a diffusion model, and the method achieves the high-precision classification of targets in a hyperspectral image through a deep learning diffusion model. Firstly, unsupervised diffusion training needs to be carried out on a diffusion model. The unsupervised diffusion process is based on a de-noising diffusion probability model (DDPM). Then, useful diffusion features are extracted from a pre-trained de-noising diffusion probability model (DDPM). And finally, inputting the features into a supervised classification model for classification. According to the hyperspectral image target classification method based on the diffusion model, the advantages of the diffusion model can be utilized, the relation in the spectral space can be captured from the hyperspectral image, effective and efficient deep features can be obtained, spectral space information is fully utilized, and high-precision classification of hyperspectral image targets is achieved.

The invention discloses a hyperspectral image classification method, device and equipment based on a dense image attention neural network, and relates to the technical field of hyperspectral image classification, and the method comprises the steps: inputting a hyperspectral image into the dense image attention neural network for feature extraction, and obtaining a topological relation attention feature, a spectral attention feature and a spatial attention feature; connecting the spectral attention characteristics with the spatial attention characteristics to obtain spectral spatial attention characteristics; fusing the topological relation attention features and the spectral space attention features to obtain classification features; and determining a classification result of the hyperspectral image based on the classification features. Through the above mode, spectral features, spatial features and topological relation features in the hyperspectral image are fully mined by using the dense graph attention neural network, different types of features in the hyperspectral image can be fully extracted, and the different types of features are effectively fused, so that reasonable application of the features is ensured, and the accuracy of the hyperspectral image is improved. Therefore, the hyperspectral image classification precision is improved, and the classification accuracy is improved.

The invention discloses an automatic silk damage area positioning and selecting method and system. The method comprises the following steps: S1, acquiring hyperspectral data; s2, generating a damage probability graph; s3, generating a physical parameter diagram; s4, collaborative segmentation: taking the damage probability graph and the physical parameter graph as input, performing fusion processing by using a spectrum-space collaborative segmentation network, and outputting a silk damage area segmentation mask after global optimization; according to the invention, a hyperspectral imaging system integrated with a multi-modal reference plate and active illumination compensation is designed according to unique properties of silk, a compensation graph is generated through pre-scanning, and local brightness compensation is carried out by using a DMD projector, so that high uniformity and high fidelity of spectral data are ensured from the source; and a stable and reliable precondition is created for subsequent accurate detection.

The invention discloses a luggage sorting and loading subsystem and a luggage sorting and loading method, the subsystem is connected with a check-in system, a conveying and loading system and a central management system, and the subsystem comprises a hyperspectral imaging module, a spectral space preprocessing pipeline, a feature extraction and fusion module, a decision module and an execution module. The hyperspectral imaging module collects luggage spectral data, the spectral space preprocessing pipeline performs smooth dimension reduction on an original spectrum, the feature extraction and fusion module fuses RGB image features and hyperspectral features, the decision module predicts a destination through a split network, and the execution module controls a mechanical arm to sort. Based on hyperspectral imaging and multi-feature fusion, the problem of a traditional recognition system in a complex environment is solved through spectral information acquisition, multi-feature fusion convolutional neural network recognition classification and a multi-modal verification mechanism, spectral and spatial feature adaptive weighted fusion is achieved, the error recognition rate is reduced, and the recognition efficiency is improved. And the identification accuracy and the processing efficiency of the airport luggage processing system are greatly improved.

The invention relates to a hyperspectral calculation imaging technology, and aims to solve the problems that in an actual remote sensing observation environment, a single imaging mode is limited by a physical sensor, high space resolution and hyperspectral resolution are difficult to obtain at the same time, and geometry and spectrum complementarity exists among multi-source images. The invention provides a hyperspectral fusion imaging method based on topology perception and a differential homeomorphic mechanism. According to the method, differential homeomorphic transformation is introduced to construct spatial degradation priori, a dual learning framework is adopted to jointly optimize high-resolution generation and bidirectional reconstruction tasks, meanwhile, a topology sensing Transform module is utilized to explicitly model a spectrum-space double-domain manifold structure, complementary correlation among multi-source data is fully mined, and the spectrum-space double-domain manifold structure is constructed. The spatial structure consistency and the spectral fidelity of the fused image are effectively improved, the problem of fusion quality reduction caused by geometric deformation and spectral variation in a complex ground feature scene is significantly improved, and a more reliable data support is provided for applications such as remote sensing monitoring and environmental perception.

The invention belongs to the technical field of open set recognition, and discloses a hyperspectral open set recognition system and method based on class semantic reconstruction. The hyperspectral open set recognition system based on class semantic reconstruction comprises a grouping spectral space reservation transformer module which comprises three stages of hierarchical structures, and each stage of hierarchical structure comprises a grouping pixel embedding module and a space enhancement feature transformation module. The spectrum-spatial feature fusion module is used for fusing the stage spectrum-spatial features into discriminative spectrum-spatial features; the class semantic reconstruction module comprises a plurality of class auto-encoders and is used for performing class semantic reconstruction on the discriminative spectral-spatial features to obtain reconstruction errors, constructing and training a classification model through the reconstruction errors and finally realizing open set recognition in combination with a multi-dimensional scoring function; according to the method, inter-class confusion can be reduced, and interference of background noise information is reduced by reconstructing semantic features instead of original pixels.

The application discloses a hyperspectral remote sensing image recognition method and device based on spectral space feature coupling, and utilizes different linear spectral filters to perform feature extraction on the whole hyperspectral remote sensing image, so that multi-class spectral features are obtained. Compared with the traditional technology, the application does not need to perform spectral dimension reduction, thereby avoiding the problem that the traditional technology weakens the subtle spectral information and high-order spectral correlation contained in the hyperspectral data. Meanwhile, the application takes the minimization of image background energy as an objective function to optimize the optimal real spectral feature vector of each linear spectral filter to the target class. Based on this, when the constructed linear spectral filter is used for feature extraction, the background energy can be minimized while the target spectral response is maintained and enhanced, so that the spectral clutter and noise in the complex environment are suppressed, and thus the detection precision of the model in the complex environment background can be improved.

This invention discloses a collaborative classification method for hyperspectral imagery and lidar data, comprising: a spectral spatial feature interaction enhancement module, a graph convolution module, a multi-head self-attention module, and a progressive feature fusion module for spectral spatial information. Specifically, firstly, deep spectral spatial information between hyperspectral images and lidar data is mined through weighted feature fusion and cross-modal convolution, extracting spatial element-level and spectral channel-level interaction features respectively. Then, a graph convolution network is used to extract spatial information from the spatial element-level and spectral channel-level interaction features, calculate spectral similarity, and construct an adjacency matrix. Next, the spatial features are learned through graph convolution operations to obtain new spatial feature representations. Then, a multi-head self-attention mechanism is used to enhance the global dependency of spectral features, fusing the extracted spatial and spectral features. Finally, a maximum-based decision fusion and progressive feature fusion strategy are employed to integrate features at different levels, obtaining the final fused features, which are then used for final classification and output. This invention acquires rich spatial-spectral features simultaneously through a spectral spatial feature interaction enhancement module, a graph convolution module, and a multi-head self-attention module. It also incorporates a spectral spatial information progressive feature fusion module to regulate the information interaction at different perception levels, thereby leveraging the synergistic advantages of multimodal information and significantly improving the accuracy and stability of land cover classification tasks.

The invention discloses a hyperspectral remote sensing image recognition method and device based on spectral spatial feature coupling, different linear spectral filters are utilized to perform feature extraction of the whole hyperspectral remote sensing image so as to obtain multi-category spectral features, and compared with a traditional technology, the hyperspectral remote sensing image recognition method does not need spectral dimension reduction, and is high in recognition efficiency. Therefore, the problem that the correlation between fine spectral information and high-order spectrum contained in the hyperspectral data is weakened in the traditional technology is avoided; meanwhile, minimization of image background energy is used as a target function to optimize the optimal real spectral feature vector of each linear spectral filter for the target category, so that when the constructed linear spectral filters are used for feature extraction, the background energy can be minimized while the target spectral response is maintained and enhanced, and the target spectral response is improved. Therefore, spectral clutter and noise in a complex environment are suppressed, and the detection precision of the model in a complex environment background can be improved.

The invention discloses an image fusion method based on a spectral space fusion state attention Mama model, and relates to the technical field of computer vision, and the method comprises the steps: obtaining hyperspectral image data and multispectral image data; the hyperspectral image data and the multispectral image data serve as input, and a high-resolution fusion reconstruction image is obtained based on output of a spectral space fusion state attention Mama model; wherein the spectrum space fusion state attention Mama model comprises a first fusion module and a double-domain convolution embedding module which are connected in sequence, the output end of the double-domain convolution embedding module is connected with a first branch and a second branch, and the output end of the first branch and the output end of the second branch are both connected with the second fusion module. According to the method, space and spectral characteristics are effectively combined, and interaction of spectral information and space information of hyperspectral and multispectral images is realized.

The application discloses a hyperspectral target detection method based on a position-sensitive Gaussian attention Transformer, relates to the technical field of computer vision, and aims at solving the problem that in the prior art, the self-attention mechanism lacks effective spatial position prior, and thus is insufficient in suppressing complex background when modeling global context, resulting in a high false alarm rate. The application sequentially performs preprocessing on a hyperspectral image, extracts spectral-spatial features, and then performs global feature enhancement through a Transformer embedded with a position-sensitive Gaussian attention mechanism, and finally outputs results through a detection head.

The invention discloses an urban land surface coverage classification system based on a multispectral remote sensing technology, and relates to the technical field of spectral spatial data calculation. The system obtains land surface temperature data of a target area and a sub-area and calculates the change rate difference of the land surface temperature data; carrying out first-level correction on the initial contribution degree by utilizing a material thermal tuning coefficient; and performing second-level space coupling correction according to the correlation coefficient of the temperature change of the marked sub-region and the adjacent region, and finally generating an accurate urban surface heat contribution report. According to the method, the transformation of the urban thermal environment contribution from static recognition to dynamic quantitative evaluation is realized, and the accuracy, fineness and practical value of an evaluation result are remarkably improved. The technical problem that the spatial correlation dynamic evaluation adjustment accuracy of urban surface classification thermal contribution is insufficient is solved.

The application discloses a hyperspectral image classification method and system based on a quaternion deep network, mainly strengthens the learning ability of the network and improves the training efficiency of the network through quaternion algebra theory and deep learning theory, and solves the problems of few hyperspectral image data samples, spectral space variability and the like.The implementation scheme is as follows: firstly, considering the high dimensionality of data, dimensionality reduction is carried out through principal component analysis.Next, a deep attention module is constructed, important features are strengthened and noise interference is suppressed, so that the quality of data is improved.Then, a multi-branch network is constructed, the strengthened features are grouped, and a quaternion generator is designed to map the hyperspectral image data to a quaternion space for processing.Meanwhile, a multi-scale quaternion attention module is embedded to further strengthen the feature expression capability.Finally, the features of each branch are fused, and high-precision classification of the hyperspectral image is realized.

The application provides a dynamic granular ball graph neural network working method for user and item recommendation, and belongs to the technical fields of recommendation systems and graph neural networks. The method comprises the following steps: S1, constructing user-side and item-side granular balls in a low-dimensional spectral space, updating the granular balls according to the node representation of each propagation layer, calculating the membership of nodes to the granular balls by using a radius-normalized distance, and defining the collaborative purity according to the interaction distribution of the granular balls on the opposite granular balls; S2, coupling fine-grained interaction modeling and coarse-grained collaborative abstraction, and performing message propagation along two complementary paths; S3, performing bilateral degree perception gating, and adaptively balancing the contributions of the two propagation paths for each node; and S4, aggregating the representations of each layer by weighted summation to obtain the final embedding of the user side and the item side, finally adopting a Bayesian personalized ranking loss to optimize the candidate item score, so as to play a good recommendation role in ranking the user and the item.

The application discloses an intracavity lipid recognition method based on spectral space joint modeling. The method comprises the following steps: collecting intravascular optical imaging data, constructing an optical imaging data set based on the intravascular optical imaging data; constructing a spectral space joint modeling network, inputting the optical imaging data set into the spectral space joint modeling network for training, and obtaining a trained spectral space joint modeling network; the spectral space joint modeling network comprises a preprocessing module, a physical layer module, a feature extraction module and a classification module connected in sequence; inputting real-time collected intravascular optical imaging data to be measured into the trained spectral space joint modeling network for processing, and obtaining a lipid probability vector composed of all A-lines corresponding to the intravascular optical imaging data to be measured. The method has the beneficial effects of explicitly utilizing spectral information, jointly modeling spectrum and deep structure, introducing the context of intra-frame and rollback direction, and being friendly to engineering implementation.

The invention provides an open set hyperspectral image accurate classification method based on difficult sample learning and dense spectral spatial feature enhancement, and belongs to the field of hyperspectral image processing. Firstly, a training sample is mapped into a dense spectral feature model, multi-layer information accumulation and fusion are achieved, spectral local changes are modeled finely, and the discrimination between adjacent categories is improved. Secondly, a spectral feature compression model is constructed, known class feature aggregation is promoted, meanwhile, a spatial modeling branch is introduced, and the sensing capacity for complex structures such as edges and textures is enhanced; and a class anchor point strategy is further adopted, so that intra-class polymerization and inter-class separability are improved, and a class center structure is stabilized. For negative samples which are difficult to distinguish, a difficult sample learning strategy is introduced, boundary learning is enhanced, and the unknown class recognition capability is improved. And finally, through the distance between the sample and the anchor point, the ground feature category is obtained, and an effective new thought is provided for open set classification of the hyperspectral image.

The application discloses a hyperspectral classification method based on a reconstructed convolutional Transformer, and belongs to the field of hyperspectral remote sensing detection, and comprises the following steps: after original image data is acquired, a spectral space reconstruction network is introduced, and separation and reconstruction operations are adopted; a scaling factor in a group normalization layer is combined to evaluate the information content of different feature maps, so that a calculation feature map is acquired; a spectral feature reconstruction network is introduced by using the redundancy of features, and segmentation transformation and fusion strategies are adopted; a spatial attention module is used to overcome the limitation caused by the grid structure of a convolution kernel, and a new activation scheme is introduced; a spectral correlation module is used to establish the connection of different pixel spectra in a hyperspectral cube, and an input feature map is reconstructed; and a linearized output is obtained by using Pooling, so that a classification result is obtained. The application realizes hyperspectral image classification with higher precision and faster speed, and can maintain stable classification performance in various complex scenes.

The application discloses a kind of semi-supervised hyperspectral image intelligent classification method, accurate ground object identification is carried out using labeled sample and unlabeled sample.The application first constructs a multiscale deformable spectral spatial feature extraction module, can extract features on multiple scales and fusion, the introduction of deformable convolution can extract more in line with the distribution characteristics of sample.Then through the proposed pseudo-label generation strategy, Gaussian function is used to weight, further improve the utilization efficiency of pseudo-label and model performance.In the training process, maintain high quantity and high quality of pseudo-label to effectively use unlabeled sample.Finally, through the transformation, long-range dependencies are captured and effective feature representations of hyperspectral remote sensing images are learned.Experimental results demonstrate the effectiveness and superiority of the application under the condition of limited labeled samples.

A kind of artificial intelligence identification model based on multi-subspace joint method of countermeasure defense, the activation value vector of input sample in each node, each layer of model is mapped to different subspace consistency, i.e. The consistency discrimination of feature embedding subspace is whether it is an attack sample;For the non-attack sample after screening, carry out multi-scale change, and compare the distribution consistency performance of each node value of each scale in model transmission, i.e. The consistency discrimination of scale space is whether it is an attack sample;For the original pixel or neural network output value of non-attack sample after further screening, carry out subspace spectral decomposition, and compare the response coefficient statistics of each spectral component, i.e. Spectral space consistency discrimination is whether it is an attack sample, to realize full spectrum defense.The present application is based on multi-incoherent space analysis, so that information is complementary, greatly improves the defense ability for attack sample, realizes full spectrum defense.

The present invention provides a fatigue performance analysis system for stir friction welding joints, which includes specimen data acquisition, fatigue loading, feature extraction, life prediction, path evaluation and visualization display modules; the system adopts a generalized regularized Transformer model to predict fatigue life, introduces an optimal convex potential function regularization term and a spectral space generalization contribution analysis mechanism to improve the prediction stability and accuracy of the model under heterogeneous data; combines a sparse optimization strategy driven by differential inclusion to reduce the overfitting risk caused by redundant channels; in addition, constructs an asymmetric pseudo-distance function and a fatigue state transfer strategy diagram, cooperates with a deep Q network to perform strategy search, and realizes high-precision estimation of remaining life; the system enhances the interpretability of fatigue path analysis results, and has good intelligence, generalization ability and engineering adaptability.

This application discloses a method, apparatus, equipment, and medium for assessing photovoltaic power plant disaster damage, belonging to the technical field of photovoltaic power plant disaster damage assessment. The method includes: acquiring pre-disaster and post-disaster image data of the target area; mapping the pre-disaster and post-disaster image data to photovoltaic panel features based on the spectral characteristics of photovoltaic modules, obtaining pre-disaster and post-disaster feature maps with positive and negative dual-polarization distribution characteristics; performing clustering on the post-disaster feature map to obtain a multimodal spectral space corresponding to the post-disaster feature map; using a preset reference probability distribution model to assign an abnormal energy score to each pixel in the post-disaster feature map; and classifying the disaster damage according to the abnormal pixels and the multimodal spectral space to generate a disaster damage classification map, wherein abnormal pixels include pixels with abnormal energy scores greater than a preset judgment threshold. This application can achieve automated and standardized identification of damaged photovoltaic areas.

The invention relates to the technical field of power transaction security risk management, and discloses a power transaction security risk management method based on big data analysis. The method comprises the following steps: establishing a transaction subject and charging period set, processing abnormal and missing data, constructing a symmetric adjacency matrix, sequentially calculating a degree matrix and a normalized Laplacian matrix, forming an initial risk vector in combination with subject historical quotation information, adaptively setting a diffusion step length and a convergence threshold by using a maximum degree value, and calculating the risk of the transaction subject according to the diffusion step length and the convergence threshold. Stable risk distribution is obtained through graph structure risk diffusion iteration, and then recognition and clustering of high-risk nodes are achieved through Laplacian spectral decomposition and spectral space clustering. Through the whole process of data acquisition, network modeling, risk quantification and dynamic diffusion to high-risk clustering, the problems of risk identification fragmentation and staticization are solved, the data accuracy and the risk tracking capability are improved, accurate hierarchical management of high-risk nodes is realized, and the manual intervention and the misjudgment rate are reduced.

The invention provides a fire range extraction method based on a nuclear combustion index and adaptive spatial spectrum optimization, and relates to the technical field of remote sensing image processing, and the method comprises the steps: collecting remote sensing image data; mapping a spectral vector corresponding to the fire image data set to a high-dimensional space based on a Gaussian radial basis kernel function, and performing kernel principal component analysis on the spectral vector to construct a kernel difference combustion index; according to kernel density estimation, performing adaptive threshold segmentation on the kernel difference combustion index to obtain an initial fire pixel detection result; constructing a spectrum space composite kernel similarity matrix, and optimizing the initial fire pixel detection result in a conditional random field according to the spectrum space composite kernel similarity matrix to obtain a transition fire pixel detection result; and performing morphological operation and small patch area filtering on the transition fire pixel detection result to obtain a predicted fire pixel detection result. According to the invention, the overall precision of fire range extraction can be improved.

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a hyperspectral remote sensing image classification method, system and device based on deep learning, and the method comprises the steps: converting three-dimensional data into one-dimensional data through generating a spectrum-space token, and carrying out the preliminary feature extraction; the to-be-processed features are decomposed into low-frequency features and high-frequency features through multi-scale wavelet decomposition, and then the low-frequency features and the high-frequency features are enhanced through channel attention processing and space attention processing; the enhanced low-frequency features are expanded along a spectral dimension, the enhanced high-frequency features are expanded along a spatial dimension, a dynamic path weight is introduced into a selective state space scanning mechanism, and adaptive feature fusion of the low-frequency features and the high-frequency features is realized; and finally obtaining a classification result according to the classification score.