10 results about "Hyperspectral image fusion" patented technology

The invention relates to the technical field of hyperspectral image processing, and particularly discloses a hyperspectral image fusion method and system based on variance guidance and heavy tail estimation, and the method comprises the steps: obtaining a low-spatial-resolution hyperspectral image and a panchromatic image; performing up-sampling on the low-spatial-resolution hyperspectral image to obtain an up-sampled image; obtaining an absolute difference value weight according to the channel-by-channel variance of the low-spatial-resolution hyperspectral image and the up-sampling image; performing channel-by-channel spatial fusion by means of absolute difference weight, and injecting high-frequency information of the panchromatic image into the up-sampling image to obtain a preliminary fusion image; performing spectral correlation correction learning on the preliminary fusion image through an attention mechanism to obtain spectral mixed output; through a feedforward network, obtaining an up-sampling image subjected to variance guide processing; and obtaining a high-spatial-resolution hyperspectral image through a residual block and convolution. According to the invention, accurate selection and fusion of the spatial-spectral features are realized.

The application discloses a kind of intraoperative tumor rapid classification system based on hyperspectral technology, comprising: image pre-processing module, for the hyperspectral image of several frequency bands of acquired tumor lesion tissue is pre-processed;Tumor lesion area identification module is used to select the hyperspectral image corresponding to multiple preferred frequencies respectively, fusion obtains fusion spectrum image, tumor target detection is carried out based on the image, and tumor lesion area is identified;Tumor lesion type identification module is used to carry out multi-pixel sampling to the tumor lesion area identified, obtains the frequency characteristics of each preferred frequency of each sampling point, and respectively input benign and malignant tumor identification model, respectively output benign and malignant possibility matrix, to identify tumor lesion type.The present application discriminates tumor benign and malignant based on tumor lesion tissue hyperspectral image, realizes the rapid classification of intraoperative tumor lesion type, reduces the rapid identification time, improves the accuracy of identification result while improving the identification efficiency.

This invention proposes a hyperspectral image fusion and reconstruction method based on cross-modal interaction and difference perception. The steps are as follows: acquiring training and testing sample sets; constructing a reconstruction network model based on cross-modal interaction and difference perception; iteratively training the reconstruction network model; and obtaining the reconstruction results of the hyperspectral image. The residual module of this invention utilizes multiple cross-modal interaction modules (CIMB) to perform cross-modal collaborative interaction on the spectral-spatial feature maps of LR-HSI and HR-MSI, enabling the mining of interaction features and contextual features between the two modalities. The difference perception fusion module (DPFM) performs difference perception and fusion on the cross-modal interaction features output by CIMB, effectively enhancing key details while mitigating distortion caused by differences between the two modal features, thus significantly improving reconstruction accuracy.

The invention provides a hyperspectral image fusion method and device for image adaptive registration, and relates to the technical field of image processing, and the method comprises the steps: taking a first hyperspectral image as a reference, carrying out the feature point extraction and matching based on the first hyperspectral image and a first multispectral image, and obtaining a projection matrix; performing projection transformation processing on the first multispectral image based on the projection matrix to obtain a second multispectral image; determining a spectral response function based on the second multispectral image and the first hyperspectral image; estimating a spectrum dictionary in the first hyperspectral image based on a VCA algorithm; estimating a sparse coefficient matrix in the first multispectral image based on the spectral response function and the spectral dictionary; constructing a second hyperspectral image based on the spectral dictionary and the sparse coefficient matrix; wherein the spatial resolution of the first multispectral image is higher than that of the first hyperspectral image. Therefore, the spatial resolution of the fused image is remarkably improved while the spectral information integrity of the hyperspectral image is kept.

The application discloses a kind of dual-branch hyperspectral spatial super-resolution network model product, training method and application, belong to image processing field.Dual-branch hyperspectral spatial super-resolution network model includes unmixing branch network, non-unmixing branch network and fusion network;Unmixing branch network includes unmixing module, abundance map super-resolution module and linear mixing module, and process output unmixing super-resolution hyperspectral image;Non-unmixing branch network includes spectral dimension reduction module and hyperspectral image super-resolution module, and process output non-unmixing super-resolution hyperspectral image;Fusion network carries out spectral dimensionality to non-unmixing super-resolution hyperspectral image, and after image and unmixing super-resolution hyperspectral image are upgraded, each waveband corresponding cascade and grouping convolution dimension reduction are carried out, and high-resolution hyperspectral image is obtained.The combination effect of air spectrum information is improved, so as to improve the quality of hyperspectral image spatial super-resolution.

The application discloses a cultivated land planting purpose change monitoring processing method based on hyperspectral remote sensing, wherein the method comprises detecting the membership of a certain crop in a certain piece of permanent basic farmland in sequence according to a crop planting detection index, and if the membership of a certain crop is greater than or equal to the planting condition membership, the current planted crop is output and reported, otherwise, the membership of different farmland uncultivated states is detected. The application can meet the conventional monitoring processing of the cultivated land planting purpose change of the permanent basic farmland, thereby relieving the long image processing time and slow response speed of the current technology after the fusion of the panchromatic image and the hyperspectral image, and effectively reducing the calculation cost.

The embodiment of the invention relates to the field of image data processing, in particular to an object image reconstruction method and device. According to the method and the device, features of an original hyperspectral image, a fusion image and a difference image are extracted, multi-scale feature extraction and fusion are further performed on hyperspectral image features and fusion image features to obtain space-spectrum fusion features, various image information is fused, and compared with a single data source, the capability of reconstructing a complex scene can be enhanced; and fusing the up-sampled low-resolution hyperspectral image with the space-spectrum fusion feature to obtain a high-resolution hyperspectral image, and considering the correlation between the multispectral image and the hyperspectral image. Compared with a traditional fusion reconstruction method and an existing super-resolution reconstruction method introducing a deep neural network, the cross-modal feature interaction capability and the multi-scale feature aggregation capability are enhanced, local detail information and a global long-distance dependency relationship of a space-spectral domain are considered at the same time, and the reconstruction quality of the high-resolution hyperspectral image is improved.

The invention discloses a multispectral and hyperspectral image fusion method and system based on a state space model, and a storage medium, and the method comprises the following steps: carrying out the preprocessing of an input low-resolution hyperspectral image and a high-resolution multispectral image, and extracting shallow features; performing enhancement processing on the shallow layer features through a spatial feature extraction module and a spectral feature extraction module to obtain intermediate layer features; the middle layer features are input into a cross fusion module, interactive fusion of space and spectral information is achieved through a cross state space model module, and preliminary fusion features are obtained; according to the method, high-frequency edge detail extraction is carried out on preliminary fusion features, the extracted high-frequency features and the preliminary fusion features are input into a cross fusion module for secondary fusion, a fused high-resolution hyperspectral image is obtained through convolution mapping, and when the hyperspectral image and a multispectral image are processed, space and spectral information is effectively reserved. Subsequent high-level visual tasks can be carried out conveniently, and the hyperspectral image fusion effect is improved.

The invention discloses a hyperspectral fusion imaging method and system of a double-branch diffusion model framework, and the method comprises the steps: inputting a hyperspectral image collected by an observation scene and a corresponding high-resolution multispectral image, and constructing a target fusion image representing model modeling; optimizing and training a one-dimensional U-Net spectrum diffusion model based on the hyperspectral image, wherein the one-dimensional U-Net spectrum diffusion model is used for learning and generating a high-fidelity spectrum component; combining generation features of a pre-training diffusion model derived from the remote sensing image with observation features extracted from the high-resolution multispectral image to obtain a high-resolution spatial component; and constructing a double-branch diffusion framework, guiding the generation process of the two components through a shared guide function, iteratively optimizing observation features, and fusing and reconstructing a high-resolution hyperspectral image. According to the method, high-quality image fusion is realized through a double-branch diffusion model generation mechanism, and an innovative solution is provided for hyperspectral image fusion imaging.

The application relates to the technical field of hyperspectral image processing, and particularly discloses a hyperspectral image fusion method and system based on variance guidance and heavy tail estimation, which comprises the following steps: acquiring a low spatial resolution hyperspectral image and a panchromatic image; performing up-sampling on the low spatial resolution hyperspectral image to obtain an up-sampled image; obtaining an absolute difference weight according to the channel-by-channel variance of the low spatial resolution hyperspectral image and the up-sampled image; performing channel-by-channel spatial fusion with the help of the absolute difference weight, injecting high-frequency information of the panchromatic image into the up-sampled image, and obtaining a preliminary fusion image; making the preliminary fusion image pass through an attention mechanism for spectral correlation correction learning to obtain a spectral mixing output; obtaining the up-sampled image subjected to variance guidance processing through a feedforward network; and obtaining a high spatial resolution hyperspectral image through a residual block and convolution. The application realizes accurate selection and fusion of spatial and spectral features.