529 results about "Hyperspectral image classification" patented technology

The present invention relates to a hyperspectral image classification method based on spectral-spatial cooperation of a deep convolutional neural network, which leads the conventional deep convolutional neural network applied to a two-dimensional image into the three-dimensional hyperspectral image classification problem. Firstly, the convolutional neural network is trained by using a small volume of label data, and a spectral-spatial feature of a hyperspectral image is autonomously extracted by using the network without carrying out any compression and dimensionality reduction processing; then, a support vector machine (SVM) classifier is trained by using the extracted spectral-spatial feature so as to classify an image; and finally, the trained neural network is combined with the trained classifier, the neural network extracts a spectral-spatial feature of a to-be-classified target and the classifier determines a specific category of the extracted spectral-spatial feature so as to acquire a structure (DCNN-SVM) that can autonomously extract the spectral-spatial feature of the hyperspectral image and carry out classification to the spectral-spatial feature, thereby forming a set of hyperspectral image classification method.

The invention discloses a hyperspectral image classification method based on fusion of multi-scale and multi-dimensional spatial spectral characteristics. The method comprises the following steps: (1)inputting a hyperspectral image; (2) preprocessing the hyperspectral images to be classified; (3) tracking neighborhood blocks; (4) generating a training set and a test set; (5) constructing a multi-scale spatial spectrum feature and multi-dimensional feature fusion network; (6) training a multi-scale spatial spectrum feature and multi-dimensional feature fusion network; and (7) classifying the test samples. The method provided by the invention can effectively solve the problems of too single feature and too single scale of the convolutional neural network during training, can solve the problem of low average classification precision AA during hyperspectral classification, can maintain the recognition capability of small sample number categories while realizing high classification precision, and is good in classification performance.

The invention discloses a hyperspectral remote sensing image SVM classification method by combining spectrum and texture features and a hyperspectral remote sensing image SVM classification system thereof. The method comprises the following steps that S1, original hyperspectral images to be classified and a ground survey data sample set are inputted; S2, the image elements of the corresponding coordinate positions in the original hyperspectral images are extracted so as to form a reference data sample set; S3, a training sample set is randomly selected for each ground feature class; S4, principal component transformation is performed, and first principal component images are extracted; S5, a region segmentation image is acquired; S6, filtering images are acquired; S7, statistics of spectrum feature information and texture feature information of each segmentation region are performed; S8, a support vector machine model is solved; S9, the original hyperspectral images are classified so that the classified hyperspectral images are obtained; and S10, the classified images are outputted. The new strategy for combining the spectrum and texture features is provided so that the hyperspectral image classification precision can be effectively enhanced.

The invention provides a hyperspectral image classification method based on superpixel sample expansion and generative adversarial network, and aims to solve the problem of low classification accuracycaused by network overfitting when the number of labeled training samples is small. The method comprises the following steps: constructing an initial training set and a test set, and performing expansion to obtain an expansion training set and a candidate test set; constructing a generative adversarial network consisting of a generator and a discriminator; using a generator to generate a false sample, using a discriminator to obtain a true and false prediction label and a category prediction label of the false sample and the extended training set; constructing loss functions of the generatorand the discriminator, and alternately training the generator and the discriminator; training a support vector machine; tnabling the candidate test set to pass through a trained discriminator and a trained support vector machine to obtain a candidate tag set; and determining category labels of the test set for the candidate label set by using a maximum voting algorithm. According to the method, the spatial features of the hyperspectral image are effectively extracted, the overfitting problem is relieved, the classification accuracy is improved, and the method can be used for carrying out ground object classification on the hyperspectral image.

The invention discloses a hyperspectral image classification method based on deep learning, and belongs to the technical field of remote sensing image processing. The method comprises the steps that 1, dimension reduction treatment on a hyperspectral image is achieved by obtaining a data sample, conducting layer-by-layer training on an autoencoder network and further adjusting an initial weight value obtained through pre-training by adopting a BP algorithm; a data cube in each pixel neighbourhood in the hyperspectral image is taken as input of a convolutional neural network, a ground object type corresponding to a pixel serves as expected output of the convolutional neural network, the convolutional neural network is trained, the trained convolutional neural network acts on the whole hyperspectral image, and a final high-precision classification result is obtained. According to the method, the defects that in a traditional hyperspectral image classification problem, details are discarded in the dimension reduction process, space information is lost in the classification process, and the classification precision is low are overcome, the good classification precision is achieved, and the method is suitable for classification of various hyperspectral images.

The invention discloses a high-spectrum image classification method based on a combined loss enhanced network, and the method is characterized in that the method comprises the following steps: 1), PCA dimension reduction; 2), spatial domain block extraction; 3), coding path feature extraction; 4), classification task training target building; 5), decoding path feature extraction and reconstruction; 6), network combined training; 7), high-spectral testing classification. The method can achieve the combination of learning reconstruction loss and classification discrimination loss functions in the same network structure in a mode of end-to-end training, so the spatial spectrum information of a high-spectral image is used efficiently, and the learning of CNN for unimportant feature variables is automatically weakened, so as to reduce the complexity of a high-spectral classification model. Meanwhile, the dependence of a high-spectrum image classification method on a label sample is reduced, and the classification precision is improved.

The invention discloses a hyper-spectral image classification method based on recurrent neural network with the object to solving the problems that in prior art, the input characteristic determination ability is weak and that the extraction of local spatial characteristics is not complete. The method comprises the following steps: 1) extracting the spatial texture characteristics and the sparse representation characteristics of a hyper-spectral image and piling and combining them as the low-level characteristics; 2) extracting from the low-level characteristics the sample local spatial sequence characteristics; 3) according to the local spatial sequence characteristics, creating a recurrent neural network model; and utilizing the training sample local spatial sequence characteristics to train the recurrent neural network model parameters; and 4) inputting the testing sample local spatial sequence characteristics into the well-trained recurrent neural network model; obtaining the highly abstract high-level semantic characteristics and obtaining the classification information of the testing sample. According to the deep learning method of the invention, the correct efficiency for hyper-spectral image classification is increased and the method can be used for vegetation investigation, disaster monitoring, map making and intelligence obtaining.

The invention discloses a hyperspectral image classification method based on a convolutional network and a cyclic neural network, and mainly solves a problem that the classification precision of a hyperspectral image is low in the prior art. The method comprises the following specific steps: (1) constructing a three-dimensional convolutional neural network; (2) constructing the cyclic neural network; (3) preprocessing a to-be-classified hyperspectral image matrix; (4) generating a training data set and a test data set; (5) training the network through the training data set; (6) extracting thespatial features and spectral features of the test data set; (7) merging the spatial features and spectral features; (8) classifying the test data set. The method introduces the three-dimensional convolutional neural network and the cyclic neural network for extracting the spatial features and spectral features of the hyperspectral image, achieves the classification through the fusion of the two types of features, and is high in classification precision of the hyperspectral image.

The invention discloses a high spectral image classification method based on NSCT transformation and DCNN. The objective of the invention is to solve the problem that texture details and directivity information of to-be-classified high spectral images cannot be sufficiently excavated in the prior art. The method comprises steps of inputting a high spectral image; carrying out NSCT transformation; carrying out normalization and block taking operation on the transformed stereo blocks; randomly selecting training, verification and test sample sets in a sample set; constructing a depth convolution neural network and setting network super-parameters; training the network; inputting the test samples into the network to obtain actual classification tags and drawing terrain classification result graph; and comparing the classification tags and the reference tags of the test samples, calculating classification evaluation indexes, drawing loss curve graphs of the training and verification samples of increasing along with the iteration times, thereby finishing the terrain classification. According to the invention, more texture details, directivity information and space information of the high spectral image are kept; the classification is quietly precise; and the method can be applied to meteorology and environmental monitoring, land utilization, urban planning and disaster prevention and reduction.

The invention discloses a hyperspectral image classification method based on a convolutional neural network and spatial spectrum information fusion. The method comprises the steps that S1 X and Y axiscoordinates of each pixel point of a hyperspectral image are extracted as spatial information, and the spatial information and spectral information are combined as the feature information of a sample; S2 training set and test set data are randomly divided; S3 training set samples including the spatial information and the spectral information are put into a one-dimensional convolutional neural network to train a classification model; and S4 the training set samples including the spatial information and the spectral information are put into the classification model for classification prediction. The convolutional neural network uses convolution kernels of different sizes to carry out convolution operation, which can effectively extract feature information with different resolutions in the spectral dimension of a hyperspectrum. In addition, the spectral dimension information and the spatial dimension information are input into the neural network to learn simultaneously. The feature of dual high resolutions of the hyperspectrum is fully used. The algorithm structure is simple, and the classification accuracy can be significantly improved.

The invention discloses a hyperspectral image classification based on gradient lifting decision tree and semi-supervise algorithm integration in order to solve the technical problem that hyperspectral image classification based on active learning and semi-supervise learning is low in classification precision. Hyperspectral image classification includes the steps that firstly, hyperspectral image data is input; secondly, features of sample points are extracted; thirdly, parameters of a gradient lifting decision tree classifier are trained; fourthly, massed learning sample points are classified; fifthly, the confidence degree of the sample points are assessed; sixthly, the sample points are screened through sparse representation; seventhly, a marked training set is updated; eighthly, a classification result is output. Assessment is conducted on the confidence degree of the unmarked sample points through the prediction result of the classifier and sparse representation, according to the confidence degree of the unmarked sample points, the sample points are divided into two sets for different kinds of processing, burdens for manual marking are reduced while classification precision is improved, and hyperspectral image classification can be used in the fields of geological survey, atmospheric pollution and like.

The invention relates to a method and a device for remote-sensed hyperspectral image classification. The method comprises the following steps of step 1: carrying out spectral angle weighted clustering based on kernel function vague C mean value on the hyperspectral image to obtain clustered indication characteristics; step 2: carrying out support vector machine (SVM) semi-supervised classification on the hyperspectral image to obtain a first classified image Image 1, and carrying out the SVM semi-supervised classification on the clustered indication characteristics to obtain a second calssified image Image 2; and step 3: establishing a clustering and SVM cooperation framework, inserting classification results of the Image 1 and the Image 2 into the clustering and SVM cooperation framework to be cooperatively analyzed so as to obtain a final hyperspectral classified image. The device comprises a clustering module, a classification module and a cooperative analysis module. The method and device for the hyperspectral image semi-supervised classification are feasible, capable of performing high-precise clustering and SVM-cooperative.

The invention discloses a spatial spectrum attention hyperspectral image classification method based on Octave convolution, and solves the problems of large class spacing, small different class spacing and low classification accuracy in the prior art. The scheme is as follows: inputting images to be classified and preprocessing data, dividing a training set and a test set, constructing an Octave convolutional neural network, determining a loss function of the Octave convolutional neural network, training and updating the Octave convolutional neural network, testing the data of the test set, and completing hyperspectral image classification. According to the method, Octave convolution operation is used to reinforce feature representation, and a spatial attention mechanism and a spectral attention mechanism are introduced, so that the network can more accurately find an area which is more beneficial to classification and contains more comprehensive and detailed information. The method ishigh in classification precision and strong in robustness, and can be applied to analysis and management of hyperspectral image data.

The invention discloses a hyperspectral image classification method based on a cooperative generation antagonistic network and a space spectrum combination. The method comprises the following steps: inputting a hyperspectral image; obtaining a sample set; generating training samples and test samples; building a multi-scale discriminator; constructing cooperative relationship; building a cooperative generation countermeasure network; the initial value of the training sample being generated by the multi-scale discriminator; the generator generating samples; a multi-scale discriminator perfromingclassification; constructing the loss function of generator and multiscale discriminator; alternatively training the generator and the multi-scale discriminator; hyperspectral images being classified. The invention utilizes the established cooperation to generate the antagonistic network, extracts the spatial spectrum combined characteristics of pixels, generates more realistic samples, increasesthe number of samples, alleviates the problems of over-fitting the network and slow convergence speed of the network, and improves the accuracy of the hyperspectral image classification.

The invention discloses a hyperspectral image classification method based on a multiclass generative adversarial network, and the method comprises the following steps: (1) inputting a hyperspectral image; (2) acquiring a sample set; (3) generating training samples and test samples; (4) constructing a multiclass generative adversarial network; (5) using a generator to generate samples; (6) classifythe training samples and the generated samples by a discriminator; (7) constructing loss functions of the generator and the discriminator; (8) alternately training the generator and the discriminator; and (9) classifying the hyperspectral image. The hyperspectral image classification method provided by the invention utilizes the constructed multiclass generative adversarial network to extract spatial features of a pixel neighborhood and generate samples to increase the number of samples at the same time, thereby enhancing a feature extraction capability of a network, alleviating the problem of network overfitting, and improving the accuracy of hyperspectral image classification.

The invention discloses a semi-supervised dimension reduction-based hyper-spectral image classification method for mainly solving the problems of high calculation quantity caused by over high hyper-spectral image data dimensions and low classification accuracy of the conventional method. The method comprises the following steps of: expressing each pixel point of a hyper-spectral image by using a feature vector, and selecting a marked training set, a test set and a total training set; constructing local inter-class and local intra-class dissimilarity matrixes of the marked training set respectively to obtain a total local dissimilarity matrix; constructing and solving a feature value equation to obtain a projection matrix; projecting the marked training set and the test set to a low-dimensional space respectively to obtain a new marked training set and a new test set; and inputting the new marked training set and the new test set to a support vector machine, and performing classification to obtain class information of the test set. By adopting the thought of semi-supervision, higher classification accuracy can be acquired; and the method can be applied to the fields of map design, vegetation survey and military intelligence acquisition.

The invention provides a high-spectral image classification method based on an improved learning model, and relates to the technical field of high-spectral images. The method comprises that one or more convolution layer and pooling layer form an extracted image characteristic alternatively, the obtained image characteristic is extracted for multiple times randomly to form different classificationmodels, the different classification models are used to carry out classification, and classification results are voted to obtain a final classification result in the principle that the minority submitto the majority, and the deep learning network model is constructed. The high-spectral image data to be classified is reconstructed, space spectrum information of high spectrum images is maintained,the sparsity of the data is increased, and thus, the high spectral image is easy to process by the classification model. The method includes fewer calculation parameters, is higher in classification accuracy, and can be used to classify natural objects in the high spectral image finely.

The invention discloses a hyperspectral image band selection method based on low-rank expression to mainly solve the problem that complexity of hyperspectral data processing is high and classification accuracy of hyperspectral images is low. The processing procedure comprises: (1) obtaining hyperspectral data and carrying out normalization processing on the data; (2) carrying out low-rank expression on the processed hyperspectral data; (3) solving low-rank expression coefficient by the use of augmented Lagrangian multiplier (ALM); (4) clustering bands according to the low-rank expression coefficient; (6) selecting a representative band from each cluster to be used as a final selected band; and (6) classifying the selected band. According to the invention, redundant information between bands is eliminated, and the band containing large amounts of information is selected. The selected band is more beneficial to classification. Classification accuracy of hyperspectral images is raised, and complexity of hyperspectral data processing is also reduced. The method provided by the invention can be used in dimensionality reduction of hyperspectral data.

The invention discloses a hyperspectral image classification method based on image regular low-rank expression dimensionality reduction. The method includes the steps that a mean shift technology is used for conducting pre-segmentation on a hyperspectral image first, image regular low-rank coefficient expression is conduced on the hyperspectral image after pre-segmentation to obtain an image regular low-rank coefficient matrix, a characteristic value equation is constructed, a mapping matrix of the dimensionality reduction is studied, and original high dimensional data are transformed to low-dimensional space to be further classified. According to the hyperspectral image classification method, a hyperspectral image local manifold structure is excavated, the spatial distribution character of an original image is kept, effective dimensionality reduction space is studied, the classification accuracy of hyperspectral images is improved, computation complexity is lowered, the problems that the dimensionality of the hyperspectral image is too high so that the calculation amount can be large, and an existing method is low in classification accuracy are mainly solved, and the hyperspectral image classification method can be used for important fields such as precision agriculture, object identification and environment monitor.

The invention discloses a local and non-local multi-feature semantics-based hyperspectral image classification method. The method mainly solves the problem in the prior art that the hyperspectral image classification is low in correct rate, poor in robustness and weak in spatial uniformity. The method comprises the steps of inputting images, extracting a plurality of features out of the images, dividing a data set into a training set and a testing set, mapping various features of all samples into corresponding semantic representations by a probabilistic support vector machine, constructing a local and non-local neighbor set, constructing a noise-reducing Markov random field model, conducting the semantic integration and the noise-reducing treatment, subjecting the semantic representations to iterative optimization, obtaining the categories of all samples based on semantic representations, and completing the accurate classification of hyperspectral images. According to the technical scheme of the invention, the multi-feature fusion is conducted, and the spatial information of images is fully excavated and utilized. In the case of small samples, the advantages of high classification accuracy, good robustness and excellent spatial consistency are realized. The method can be applied to the fields of military detection, map plotting, vegetation investigation, mineral detection and the like.

The invention discloses a nuclear coordinated expression-based hyperspectral image classification method. The method comprises the following steps: carrying out feature selection by adopting a waveband selection strategy with strong operability; carrying out local binary-pattern space feature extraction on the basis of selected different feature groups; carrying out nuclear coordinated expression classification; and finally fusing classification results corresponding to the groups of features and a residual-level fusion strategy and obtaining the final high-precision classification result. According to the method, the textural features of data extracted by a LBP operator are combined, the LBP has the remarkable advantages of rotation invariance and grey level invariance, and the calculation is simple, so that the robustness of the features to be classified is further increased. Finally, a nuclear coordinated expression classifier is used for classification, the calculation efficiency is better than that of the traditional sparse manner and the nonlinear space data can be classified, so that the application range is wider and the application performance is more excellent.

The invention relates to the technical field of hyperspectral image classification, and discloses a hyperspectral image deep learning classification method, device and equipment and a storage medium,which are used for improving the accuracy and efficiency of hyperspectral image classification. The method comprises the following steps: acquiring a to-be-classified hyperspectral image; carrying outrandom clipping on a to-be-classified hyperspectral image according to a preset window size and the marked sample set to obtain a to-be-trained sample set; expanding the data set through image transformation to obtain a corresponding deep learning sample set; extracting spatial spectrum features by adopting a convolutional recurrent neural network and a three-dimensional convolutional neural network; and classifying the hyperspectral images through a preset neural network classification model obtained through training to obtain a corresponding image classification result. By constructing thedeep neural network model, the deep abstract features of the hyperspectral image can be automatically extracted, the workload of manual feature extraction and optimization is effectively reduced, andthe end-to-end automatic identification and classification of the hyperspectral image are realized.

The invention discloses a hyperspectral image classification method based on linear regression Fisher discrimination dictionary learning (LRFDDL). The method includes the following steps that the textural features and the spectral features of each hyperspectral image are fused and normalized; a dictionary and a linear classifier are obtained by learning through an LRFDDL algorithm; model prediction is conducted. According to the hyperspectral image classification method based on LRFDDL, the LRFDDL algorithm is applied to hyperspectral image classification, so that the classification accuracy of the hyperspectral images is improved; in addition, the textural features of the hyperspectral images are added into the classification, and thus the classification effect of the hyperspectral images is further improved.

The invention discloses a hyper-spectral image classification method based on ridgelet and a depth convolution network, and mainly aims to solve the problem that the accuracy is low and the computational complexity is high for hyper-spectral image classification through the existing technology. The method comprises the following steps: (1) selecting training samples in a hyper-spectral image; (2) extracting spectral information and spatial information of the training samples; (3) forming training sample sets based on the spectral information and spatial information; (4) constructing a five-layer depth convolution network, and designing a ridgelet filter to initialize the network; (5) using the training sample sets to train a constructed neural network; and (6) using the trained neural network to classify the other training samples, thus completing image classification. The method has the advantages of high classification accuracy and high classification speed, and can be used in weather monitoring, environmental monitoring, urban planning, and disaster prevention and mitigation.

The invention provides a hyperspectral image classification method based on combined multi-level spatial spectrum information CNN, and mainly solves the problems of poor classification performance andpoor classification area consistency of hyper-spectral images. The method comprises the following implementation steps: inputting a hyperspectral data set; constructing a convolutional neural networkand a multi-stage spatial spectrum information extraction network; generating a combined multi-level spatial spectrum information convolutional neural network CNN; inputting a training sample set, and training the network by using a loss function; and inputting the test data set, and classifying the hyperspectral image by using the trained combined multi-level spatial spectrum information convolutional neural network CNN. According to the invention, the built combined multi-stage spatial spectrum information convolutional neural network CNN is used; according to the method, the multi-level spatial information and the global inter-spectral information of the hyperspectral image can be extracted and fused, the problems that in the prior art, spatial feature information is not fully utilized, a convolution kernel cannot extract the spectral global information, and consequently the consistency of classification areas is poor and the precision is not high are solved, and the classificationaccuracy of the hyperspectral image is improved.

The invention discloses a hyperspectral image classification method for a lightweight depth separable convolution feature fusion network, and the method comprises the steps: processing a hyperspectralimage, carrying out the normalization processing to obtain a sample set, carrying out the classification of the sample set, and completing the data preprocessing; setting a spectral information extraction module, a spatial information extraction module and a multi-layer feature fusion module to complete the construction of a training model; training the preprocessed convolutional neural network by using the constructed training model to obtain a final training result; repeating the operation of the convolutional neural network for N times, carrying out voting through N test results to obtaina final classification result, and carrying out hyperspectral image classification; and outputting a classification image according to the hyperspectral image classification result. According to the method, the spectral information and the spatial information are fused, the number of parameters is reduced, the network depth is increased, the network operation efficiency is improved, and the classification accuracy is improved.

The invention discloses a hyperspectral image classification method based on a multi-task low rank. The method mainly solves the problems that an existing method only uses spectral characteristics in the hyperspectral image classification process, hyperspectral characteristics cannot be described from multiple angles, and therefore the classification accuracy is low. The method includes the steps that firstly, a hyperspectral image is input; secondly, spectrum gradient characteristics of the hyperspectral image are extracted; thirdly, the spectral characteristics and the spectrum gradient characteristics serve as input signals and dictionaries of a multi-task low rank model, the model is resolved, and then two coefficient matrixes are acquired; fourthly, the two coefficient matrixes are connected according to lines, and a new coefficient matrix is acquired and serves as a new characteristic vector matrix of samples; fifthly, one part of the samples are selected as training sets, and the other part of the samples serve as test sets; sixthly, the training sets and the test sets are input in a sparse representation classifier, and then a classification result is acquired. Compared with a traditional low-rank model classification method, cross characteristic information is effectively utilized, and compared with an exiting image classification method, the high classification accuracy is acquired.

The invention relates to an image classification method based on a multi-scale dense convolutional neural network and a spectral attention mechanism. The multi-scale dense convolutional neural networkis constructed by using a dense connection mechanism, and the dense connection mechanism can effectively alleviate the problem of gradient disappearance, enhance feature propagation, encourage feature multiplexing, greatly reduce the number of parameters, and reduce the demand for training samples in the network training process; In addition, the network is combined with a spectrum attention mechanism, and the characteristic utilization of the spectrum direction is more effective. According to the method, autonomous extraction and high-precision classification of hyperspectral image depth features are realized under the condition of small samples. Compared with an existing hyperspectral image classification method based on deep learning, the hyperspectral image classification method has the advantages that the sample demand is lower, and the precision is higher.

The invention discloses a hyperspectral image classification method based on compression spectrum clustering integration. The hyperspectral image classification method comprises a classification process including the steps that (1) image characteristic sets of hyperspectral images are obtained; (2) an image characteristic set sub space subjected to dimension reduction is obtained; (3) a plurality of hyperspectral image dividing results are obtained; (4) the final hyperspectral image dividing result is obtained; (5) the hyperspectral image classification result is obtained; (6) the accurate classification of the hyperspectral image is obtained. Compared with the prior art, the hyperspectral image classification method has the advantages that the defect that a k-means algorithm adopted in the classical spectral clustering is sensitive to initialization is overcome; the characteristic dimension of the hyperspectral images is reduced; meanwhile, the classification precision is obviously improved, and the dividing effect is good.

The invention discloses a hyperspectral image classification method based on a deep learning space-spectrum joint network, and the method comprises the steps: firstly carrying out the data partitioning of an original hyperspectral image, and then training the deep learning space-spectrum joint network through a small amount of label data; simultaneously carrying out spectral dimension feature extraction processing on the input hyperspectral original image by a bidirectional long-short-term memory model with an attention mechanism and a 1D hole convolutional neural network to obtain a final spectral feature map; performing data normalization processing on an input image, performing PCA dimension reduction, extracting input features, sending the input features into a multi-scale multi-levelfilter convolutional network to extract spatial features, and performing global average pooling layer processing to obtain a final spatial feature map; and finally, carrying out classification by combining the trained network parameters. According to the method, spectral dimension features and spatial features are processed separately, richer and more effective spectral feature maps and richer feature expressions can be obtained, and the classification precision is further improved.