Hyperspectral image classification using FPCA-based kernel extreme learning machine

In recent studies, superpixel segmentation has been integrated into hyperspectral (HS) image classification methods. However, the existing superpixel-based classification methods usually suffer from two serious problems. First, the accuracy and efficiency of current superpixel segmentation approaches cannot meet the demands of practical applications for HS images; second, conventional superpixel-based classification methods generally consider each generated superpixel as a unit for the image classification, which may help to reduce the computing time but result in a significant decrease of the classification accuracy. To solve the problems, we propose a fast region growing based superpixel segmentation (FRGSS) algorithm and a novel texture-adaptive superpixel integration strategy (TASIS) for the HS image classification. Experimental results on real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) HS images demonstrate that the proposed FRGSS outperforms the state-of-the-art superpixel algorithm. In addition, the superiority of the TASIS is verified compared to the pixel-wise and the conventional superpixel-based classification methods.

Most hyperspectral image (HSI) classification methods assume that all classes in the test set are present during training. However, in real-world applications, acquiring labeled training samples is challenging. As a result, it is difficult for the training dataset to cover all possible land cover types, leading to the generalized zero-shot learning (GZSL) problem. Recently, vision-language models (VLMs) have provided rich semantic priors for land cover classes, offering promising potential for GZSL. However, two fundamental gaps hinder their application to HSI classification: the task paradigm gap, arising from the difference between image-level VLMs and the pixel-level HSI classification task; and the knowledge gap, due to the inconsistency between VLM features and HSI spectral–spatial representations. To bridge both gaps, a novel framework leveraging VLM semantic priors for GZSL in HSI classification is proposed, primarily using pseudo-labeling technique to provide knowledge for unseen classes. Specifically, a pseudo-label generation and enhancement module enables a paradigm transition from image-level understanding to pixel-level classification by incorporating HSI’s spatial information. A pseudo-label correction module then refines noisy labels using spectral cues to address the knowledge gap. Finally, a global learning strategy integrates pseudo-label distillation, supervised learning, and feature regularization to classify seen classes while enabling generalization to unseen ones. Experiments on benchmark HSI datasets demonstrate the proposed method’s superiority in generalized zero-shot classification. This work highlights the potential of VLMs in advancing HSI classification in practical applications.

Hyperspectral image classification using multi-feature fusion

Recently, hyperspectral image (HSI) classification methods based on deep learning have demonstrated excellent performance. However, these deep learning methods still face two major challenges. One is that they require a large number of labeled samples, and the other is that training parameters takes a lot of time. In this paper, we propose a dual-branch deep stochastic adaptive Fourier decomposition (SAFD) network (DSAFDNet) to alleviate the aforementioned two issues in HSI classification applications. SAFD is a newly developed signal processing tool with solid mathematical foundation. It can be used to find common filters (i.e. convolution kernels) of a set of random signals or multi-signals. Since the convolution kernels obtained by SAFD decomposition are complex numbers, few deep learning methods directly deal with such complex convolution kernels. To this end, we propose a dual-branch network to extract deep features from hyperspectral images using both real and imaginary parts of convolutional kernels. After deep feature extraction using DSAFDNet, we further investigate the classification performance of different classifiers on the extracted features. Experimental results show that the proposed method outperforms some HSI classification methods with similar principles. Moreover, compared with other state-of-the-art deep learning methods, the proposed method can achieve better classification performance.

Spectral features cannot effectively reflect the differences among the ground objects and distinguish their boundaries in hyperspectral image (HSI) classification. Multi-scale feature extraction can solve this problem and improve the accuracy of HSI classification. The Gaussian pyramid can effectively decompose HSI into multi-scale structures, and efficiently extract features of different scales by stepwise filtering and downsampling. Therefore, this paper proposed a Gaussian pyramid based multi-scale feature extraction (MSFE) classification method for HSI. First, the HSI is decomposed into several Gaussian pyramids to extract multi-scale features. Second, we construct probability maps in each layer of the Gaussian pyramid and employ edge-preserving filtering (EPF) algorithms to further optimize the details. Finally, the final classification map is acquired by a majority voting method. Compared with other spectral-spatial classification methods, the proposed method can not only extract the characteristics of different scales, but also can better preserve detailed structures and the edge regions of the image. Experiments performed on three real hyperspectral datasets show that the proposed method can achieve competitive classification accuracy.

In this paper, we propose a hyperspectral image (HSI) classification method using spectral-spatial long short term memory (LSTM) networks. Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature. Meanwhile, we firstly use principle component analysis (PCA) to extract the first principle component from a HSI, and then select local image patches centered at each pixel from it. After that, we feed the row vectors of each image patch into Spatial LSTM one by one to learn the spatial feature for the center pixel. In the classification stage, the spectral and spatial features of each pixel are fed into softmax classifiers respectively to derive two different results, and a decision fusion strategy is further used to obtain a joint spectral-spatial results. Experiments are conducted on two widely used HSIs, and the results show that our method can achieve higher performance than other state-of-the-art methods.

Hyperspectral image (HSI) classification has attracted much attention and extensive research efforts over the past decade. Due to few labeled samples versus high dimensional features, it is a challenging problem in practice. Recently, combining the pixel spectral information and the spatial (neighborhood) information has been verified to be effective for HSI classification. In this paper, we introduce a novel method for HSI classification using set-to-set distance (SSD). Based on the assumption that neighbor pixels tend to belong to the same class with high probability, we model a test pixel and its neighbor pixels as a testing set (or a neighbor set) inspired by bilateral filtering. Meanwhile, the training pixels belong to the same class are modeled as a training set. Therefore, the classification is based on comparisons of sets distances. Experiments on a real HSI dataset show that our proposed method outperforms a number of existing state-of-the-art approaches.

Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples. In this paper, we present an HSI classification architecture called camera spectral response network (CSR-Net), which can learn the optimal camera spectral response (CSR) function for HSI classification problems and effectively reduce the spectral dimensions of HSI. Specifically, we design a convolutional layer to simulate the capturing process of cameras, which learns the optimal CSR function for HSI classification. Then, spectral and spatial features are further extracted by spectral and spatial attention modules. On one hand, the learned CSR can be implemented physically and directly used to capture scenes, which makes the image acquisition process more convenient. On the other hand, compared with ordinary HSIs, we only need images with far fewer bands, without sacrificing the classification precision and avoiding the curse of dimensionality. The experimental results of four popular public hyperspectral datasets show that our method, with only a few image bands, outperforms state-of-the-art HSI classification methods which utilize the full spectral bands of images.

Convolutional neural networks (CNNs), a kind of feedforward neural network with a deep structure, are one of the representative methods in hyperspectral image (HSI) classification. However, redundant information and interclass interference are common and challenging problems in HSI classification. In addition, if the spectral and spatial information is not properly extracted and analyzed, it will affect the classification performance of the network to a great extent. Aiming at these issues, this article proposes an HSI classification method based on an adaptive hash attention mechanism and a lower triangular network (AHA-LT). First, the attention mechanism is introduced in the preprocessing stage, which is composed of the spectral attention module and the adaptive hash spatial attention module in series. Then, the data processed by the attention mechanism are introduced into the lower triangular network (LTNet) to obtain the fused high-dimensional semantic features. Finally, we compress the features and obtain the output classification results through several fully connected layers. Among them, LTNet is composed of 2-D–3-D CNN and multiscale features. The network integrates the characteristics of multibranch, feature fusion, feature compression, and skip connections. Extensive experiments on four widely used HSI data sets show that the proposed method can obtain a great improvement in performance compared with the existing methods.

Recently, collaborative representation has received much attention in the hyperspectral image (HSI) classification due to its simplicity and effectiveness. However, the existing collaborative representation-based HSI classification methods ignore the correlation among different classes. To overcome this problem, we propose a discriminative kernel collaborative representation and Tikhonov regularization method (DKCRT) for HSI classification, which can make the kernel collaborative representation of different classes to be more discriminative. Specifically, the kernel trick is adopted to map the original HSI into a high space to improve the class separability. Besides, distance-weighted kernel Tikhonov regularization is adopted to enforce these training samples to have large representation coefficients, which are similar to the test sample in the high-dimensional feature space. Moreover, we add a discriminative regularization term to further enhance the separability of different classes, which can take the correlation among different classes into consideration. Furthermore, to take the spatial information of HSI into consideration, we extend the DKCRT to a joint version named JDKCRT. Experiments on real HSIs demonstrate the efficiency of the proposed DKCRT and JDKCRT.

Hyperspectral Image (HSI) classification plays a key role remote sensing field. Recently, deep learning has demonstrated its effectiveness in HSI Classification field. This paper presents a spectral-spatial HSI classification technique established on the deep learning based deep belief network (DBN) for deep and abstract feature extraction and adaptive boundary adjustment based segmentation. Proposed approach focuses on integrating the deep learning based spectral features and segmentation based spatial features into a framework for improved performance. Specifically, first the deep DBN model is exploited as a spectral feature extraction based classifier to extract the deep spectral features. Second, spatial contextual features are obtained by utilizing effective adaptive boundary adjustment based segmentation technique. Finally, maximum voting based criteria is operated to integrate the results of extracted spectral and spatial information for improved HSI classification. In general, exploiting spectral features from DBN process and spatial features from segmentation and integration of spectral and spatial information by maximum voting based criteria, has a substantial effect on the performance of HSI classification. Experimental performance on real and widely used hyperspectral data sets with different contexts and resolutions demonstrates the accuracy of the proposed technique and performance is comparable to several recently proposed HSI classification techniques.

In recent years, benefiting from the rapid development of deep learning technology in the field of computer vision, the study of hyperspectral image (HSI) classification has also made great progress. However, compared with ordinary RGB images, HSIs are more like 3D cubes; therefore, it is necessary and beneficial to explore classification methods suitable for the very special data structure of HSIs. In this paper, we propose Multiple Spectral Resolution 3D Convolutional Neural Network (MSR-3DCNN) for HSI classification tasks. In MSR-3DCNN, we expand the idea of multi-scale feature fusion and dilated convolution from the spatial dimension to the spectral dimension, and combine 3D convolution and residual connection; therefore, it can better adapt to the 3D cubic form of hyperspectral data and make efficient use of spectral information in different bands. Experimental results on four benchmark datasets show the effectiveness of the proposed approach and its superiority as compared with some state-of-the-art (SOTA) HSI classification methods.

In this paper, a novel method based on wavelet transformation of functional data for accurate classification of hyperspectral images is proposed. The motivation of the proposed method is that in the hyperspectral images, the spectral curve of each pixel can be viewed as a function, mathematically. And it can make the best of the abundant spectral information of the original hyperspectral images. Based on this, we have proposed an effective hyperspectral image classification method which mainly used functional principal component analysis (FPCA) before. However, FPCA method focuses on the global features analysis of hyperspectral image data and may not well extract local structural features. In this paper, in order to better reflect the local structural features of hyperspectral images, we make use of the time-frequency localization characteristics of wavelet transformation to decompose hyperspectral images. Then, the sparse wavelet decomposition coefficients are classified by using extreme learning machine (ELM). Experimental results on two popular hyperspectral images show that our method outperforms some state-of-the-art hyperspectral image classification methods.

Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hyperspectral images by using kernel methods is investigated. For a given hyperspectral image, the principle component analysis (PCA) transform is first performed. Then, the first principle component of the input image is segmented into non-overlapping homogeneous regions by using the entropy rate superpixel (ERS) algorithm. Next, the local spectral histogram model is applied to each homogeneous region to obtain the corresponding texture features. Because this step is performed within each homogenous region, instead of within a fixed-size image window, the obtained local texture features in the image are more accurate, which can effectively benefit the improvement of classification accuracy. In the following step, a contextual spectral-texture kernel is constructed by combining spectral information in the image and the extracted texture information using the linearity property of the kernel methods. Finally, the classification map is achieved by the support vector machines (SVM) classifier using the proposed spectral-texture kernel. Experiments on two benchmark airborne hyperspectral datasets demonstrate that our method can effectively improve classification accuracies, even though only a very limited training sample is available. Specifically, our method can achieve from 8.26% to 15.1% higher in terms of overall accuracy than the traditional SVM classifier. The performance of our method was further compared to several state-of-the-art classification methods of hyperspectral images using objective quantitative measures and a visual qualitative evaluation.

Hyperspectral image classification is a challenging problem for machine learning methods due to the small number of labelled samples and high spectral variability. In this paper, to solve this problem, a novel superpixel and multi-classifier fusion (SMCF)-based classification method for hyperspectral images is proposed. This method takes full advantage of the spectral information of superpixels and the spatial information of hyperspectral images and includes the following three steps. First, superpixels are used to increase the number of training samples and their spectral diversity. Second, label propagation (LP) is used to classify the hyperspectral images. Although LP is an efficient semi-supervised classification method, the corresponding performance is poor for certain land cover types with dispersed spatial distributions. Thus, a support vector machine (SVM) classifier is introduced to classify the hyperspectral images. Finally, the results of the SVM and LP classifiers are combined using our new class-specific weighted fusion algorithm. In the experiments, we selected three widely used and real hyperspectral data sets for evaluation. The final classification performance was evaluated based on two common metrics: the overall accuracy (OA) and the Kappa coefficient. The experimental results show that the proposed SMCF method is superior to six well-known classification methods, even when only 1% or less of the labelled samples are used.