Hyperspectral Image Classification Based on Hierarchical SVM Algorithm for Improving Overall Accuracy

Combining spectral and spatial information can significantly improve the classification performance of hyperspectral image (HSI). Currently, a lot of spectral–spatial HSI classification methods have been proposed. However, the task of HSI classification has remained challenging since the number of training samples is limited in real scenarios. In this article, we propose a novel HSI classification framework with single sample, in which the spectral self-similarity and spatial polygon structure information are fully combined to improve the classification performance. On the one hand, spectral self-similarity is used to expand training samples, which makes it possible to obtain sufficient samples with minimal cost. On the other hand, polygonal partition is introduced to acquire the geometrical structure of land covers in man-made environments. Specifically, the edge information of geometric objects is captured by polygonal partition, which can be utilized to constrain the spatial range of sample expansion and optimize the classification results. Experimental results on three real HSIs illustrate that the proposed method performs very well under small training sample size even when the number of samples is single per class.

One of the most important problems in classification of Hyperspectral images is Hughes phenomena. The main reason for this problem is high dimension feature space and deficiency of training samples. To solve this kind of problem, so many methods are applied which some of them emphasize feature reduction and the others point to classification method with low sensitivity to training samples or consider spatial information or semi supervised training samples. In feature reduction algorithms intensity vectors of pixels are used individually. Additionally, in most of such techniques each component of intensity vector is considered as an individual element without attention to inner relation among them. In this paper we consider members of intensity vector as sampling points of spectral reflecting curve (SRC) of corresponding land cover (LC) and use fractal features of SRC in order to classify the picture. Moreover, we will show using these features improve the classification results in multispectral images.

Recently, deep learning-based methods have made great progress in hyperspectral image (HSI) classification (HSIC). Different from ordinary images, the intrinsic complexity of HSIs data still limits the performance of many common convolutional neural network (CNN) models. Thus, the network architecture becomes more and more complex to extract discriminative spectral-spatial features. For instance, 3-D CNN usually has a large number of trainable parameters, thus increasing the computational complexity of the HSIC. In this letter, we designed a cooperative spectral-spatial attention dense network (CS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ADN) that takes raw 3-D HSI data as input data. Specifically, the attention module consists of spectral and spatial axes, by which the salient spectral-spatial features will be emphasized. Furthermore, we combined these attention modules with the dense connection, which is termed as the lightweight dense block; it has a lower computation cost and achieves better classification performance. At the same time, we introduced the center loss, by jointly using the supervision of the center loss and the softmax loss, where the discriminative features could be clearly observed, particularly for small data sets. Experimental results on the biased and unbiased HSI data show that our method outperforms several state-of-the-art methods in HSIC with small training samples.

This paper discusses about a sparsity based algorithm used for Hyperspectral Image (HSI) classification where the test pixel vectors are sparsely represented as the linear combination of a few number of training samples from a well-organised dictionary matrix. The sparse vector is obtained using Basis Pursuit (BP) which is a constrained l4 minimization problem. This problem is solved by using a simple and powerful iterative algorithm known as Alternating Direction Method of Multipliers (ADMM) which significantly reduces the computational complexity of the problem and thereby speeds up the convergence. The classification accuracy is considerably improved by including efficient preprocessing techniques to remove the unwanted information (noise) present in Hyperspectral images. This paper uses a fast and reliable denoising technique based on Legendre Fenchel Transformation (LFT) to effectively denoise each band of HSI prior to ADMM based classification (proposed method). A comparison of proposed technique with one of the convex optimization tools namely, CVX is given to exhibit the fast convergence of the former method. The experiment is performed on standard Indian Pines dataset captured using AVIRIS sensor. The potential of the proposed method is illustrated by analyzing the classification indices obtained with and without applying any preprocessing methods. With only 10% training set, an overall accuracy of 96.76% is obtained for the proposed method at a much faster rate compared to computation time taken by CVX solver.

Hyperspectral image (HSI) classification has been extensively applied for analyzing remotely sensed images. HSI data consist of multiple bands that provide abundant spatial information. Convolutional neural networks (CNNs) have emerged as powerful deep learning methods for processing visual data. In recent work, CNNs have shown impressive results in HSI classification. In this paper, we propose a hierarchical neural network architecture called feature extraction with hybrid spectral CNN (FE-HybridSN) to extract superior spectral–spatial features. FE-HybridSN effectively captures more spectral–spatial information while reducing computational complexity. Considering the prevalent issue of class imbalance in experimental datasets (IP, UP, SV) and real-world hyperspectral datasets, we apply the focal loss to mitigate these problems. The focal loss reconstructs the loss function and facilitates effective achievement of the aforementioned goals. We propose a framework (FEHN-FL) that combines FE-HybridSN and the focal loss for HSI classification and then conduct extensive HSI classification experiments using three remote sensing datasets: Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SV). Using cross-entropy loss as a baseline, we assess the hyperspectral classification performance of various backbone networks and examine the influence of different spatial sizes on classification accuracy. After incorporating focal loss as our loss function, we not only compare the classification performance of the FE-HybridSN backbone network under different loss functions but also evaluate their convergence rates during training. The proposed classification framework demonstrates satisfactory performance compared to state-of-the-art end-to-end deep-learning-based methods, such as 2D-CNN, 3D-CNN, etc.

The lack of training samples remains one of the major obstacles in applying convolutional neural networks (CNNs) to the hyperspectral image (HSI) classification. In this letter, the accurate classification of HSI with limited training samples is investigated. Due to the advantages of minimizing the distance between samples in the same class and maximizing the distance between samples in different classes, siamese CNN is used for HSI classification with limited training samples. After that, to improve the classification performance, data augmentation is investigated for siamese CNN-based HSI classification. Specifically, pair data augmentation based on CutMix is proposed to generate the training pairs of the same or different classes and the new generated training pairs are used to train siamese CNN. Traditional data augmentation methods simply generate new training samples. It is not proper when data augmentation methods keep the loss function and change the content of the input at the same time. Therefore, a soft-loss-based siamese CNN, which changes its loss according to the coupled replacement data augmentation, is proposed to further address the HSI classification with limited training samples. In the experimental part, on two widely used hyperspectral datasets, the influences of different training samples and window sizes are discussed, and the experimental results reveal that the proposed soft-augmentation-based siamese CNN provides competitive results with limited training samples compared with state-of-the-art methods.

Self-adaptive differential evolution for feature selection in hyperspectral image data

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.

This paper reports the scaling behavior of the fuzzy c-means based classifier (FCMC) with many parameters. FCMC is a classifier based on clustering approaches. The classification accuracy on test sets (i.e., the generalization capability) is not necessarily improved by increasing the number of clusters. Especially when the number of training samples is relatively small, not only the classification boundary over-fits the data, but also covariance matrices and cluster centers are computed incorrectly, since the number of samples in each cluster becomes smaller. Hence, the test set accuracy deteriorates. The performance of FCMC with two clusters in each class and the number of training samples less than 1000, was reported in the literature. This paper reports the scaling behavior of FCMC by testing with variously-sized training samples. The number of clusters of FCMC is increased up to eight. The number of clusters used in this paper is not very large but the number of parameters is relatively large. So, the parameters are optimized to training sets. LibSVM is one of the widely known state of the art tools for support vector machines (SVM). The test set accuracy, training time and testing time (i.e., the detection time) of FCMC are compared with LibSVM by varying the size of training sets. FCMC shows a good generalization capability, though the parameters are optimized to training sets. When the number of training samples is increased by 10 times, the training time of FCMC increases by 10 times, but that of LibSVM increases by a factor of 100. The testing time is also much shorter than LibSVM when the size of the training set is large.

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.

Achieving high classification accuracy under a limited number of training samples remains a key challenge in hyperspectral image (HSI) classification. To address this issue, we propose a random replacement data augmentation method. The proposed method randomly selects a rectangular spatial region in HSI, and the pixels in the selected region are replaced with data from different spectral dimensions randomly to generate new sample, increasing the diversity of training samples effectively. In addition, we also propose an improved deep metric learning-based relation network, which improves model’s ability to extract deep features and fully utilizes spatial information to establish relationships between feature maps and categories effectively. Experiments have shown that the proposed method can achieve more competitive results.

Hyperspectral images belong to high-dimensional data having a lot of redundancy information when they are directly used to classification. Support vector machine (SVM) can be employed to map hyperspectral data to high dimensional space effectively and make them linearly separable. In this paper, spectral and spatial information of hyperspectral images were used to construct SVM kernel function respectively. This paper proposed a hyperspectral image classification method utilization spatial-spectral combined kernel SVM in order to improve classification accuracy. The proposed method was used to classify AVIRIS hyperspectral images. The results demonstrated that the proposed SVM method can achieve 96.13% overall accuracy for the single category classification and 84.81% overall accuracy for multi-class classification only using ten percent of the total samples as the training samples. That is to say, the proposed method can make full use of the spectral information and spatial information of hyperspectral data, and effectively distinguish different categories compared with the traditional SVM for classification.

Recently, multiple kernel learning (MKL) methods have been developed to improve the flexibility of kernel-based learning machine. The MKL methods generally focus on determining key kernels to be preserved and their significance in optimal kernel combination. Unfortunately, computational demand of finding the optimal combination is prohibitive when the number of training samples and kernels increase rapidly, particularly for hyperspectral remote sensing data. In this paper, we address the MKL for classification in hyperspectral images by extracting the most variation from the space spanned by multiple kernels and propose a representative MKL (RMKL) algorithm. The core idea embedded in the algorithm is to determine the kernels to be preserved and their weights according to statistical significance instead of time-consuming search for optimal kernel combination. The noticeable merits of RMKL consist that it greatly reduces the computational load for searching optimal combination of basis kernels and has no limitation from strict selection of basis kernels like most MKL algorithms do; meanwhile, RMKL keeps excellent properties of MKL in terms of both good classification accuracy and interpretability. Experiments are conducted on different real hyperspectral data, and the corresponding experimental results show that RMKL algorithm provides the best performances to date among several the state-of-the-art algorithms while demonstrating satisfactory computational efficiency.

Recently, depth methods have developed rapidly and achieved remarkable achievements in the field of hyperspectral image classification. Deep learning largely depends on high-quality labels. However, for hyperspectral images, obtaining high-quality labels is difficult, expensive and time-consuming. For the problem of the small number of training samples, We propose a Spatial-spectral Siamese network, which randomly selects a number of tasks composed of support sets and query sets on the training set. The network is used to extract spectral and spatial features and fuse them to obtain spatial-spectral features. The fused Spatial-spectral features are trained to optimize the Spatial-spectral Siamese network by contrastive loss, and the similarity loss is used to measure the similarity score between samples to optimize the similarity network. In addition, the cluster labels obtained by clustering are used as supervised information, The performance of network classification is further optimized by monitoring the clustering task and using a large amount of unlabeled sample information. The results of two frequently used hyperspectral data sets show that the proposed method still has good performance and effect in classification accuracy when training samples are very small.

Feature extraction (FE) methods based on low-rank representation (LRR) have become important topics in hyperspectral images (HSIs) data analysis. In this paper, a supervised FE method for HSIs data based on LRR with the ability to preserve the local pairwise constraints information (LRLPC) is proposed. LRLPC does not change the data dimensionality and only employs a technique to enrich the original feature space (OFS) and to obtain enriched feature space, which results in features richer than OFS. To overcome the problem of LRR in lacking the local structure information (LSI) of data, a local discriminative regularization term is imposed on the fitness function of LRR to keep the LSI of data. For nonlinear structure of data, LRLPC is extended to kernel LRLPC (KLRLPC) using kernel trick. Utilization of existing information in the pairwise constraints is useful for limited labeled samples situations as a common problem in HSI data analysis. The obtained experimental results using two well-known HSI data sets confirm the effectiveness of LRLPC and KLRLPC for dimension reduction and classification of HSIs.