Query: Hyperspectral images classification

title: Overview of Hyperspectral Image Classification
abstract: With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

title: Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
abstract: Convolutional neural network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, and thus, they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed graph convolutional network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non Euclidean data and is applicable to the irregular image regions represented by graph topological information. Different from the commonly used GCN models that work on a fixed graph, we enable the graph to be dynamically updated along with the graph convolution process so that these two steps can be benefited from each other to gradually produce the discriminative embedded features as well as a refined graph. Moreover, to comprehensively deploy the multiscale information inherited by hyperspectral images, we establish multiple input graphs with different neighborhood scales to extensively exploit the diversified spectral spatial correlations at multiple scales. Therefore, our method is termed multiscale dynamic GCN (MDGCN) The experimental results on three typical benchmark data sets firmly demonstrate the superiority of the proposed MDGCN to other state of the art methods in both qualitative and quantitative aspects.

title: Small Sample Classification of Hyperspectral Remote Sensing Images Based on Sequential Joint Deeping Learning Model
abstract: Although hyperspectral remote sensing images have rich spectral features, for small samples of remote sensing images, feature selection, feature mining, and feature integration are very important. A single model is difficult to apply to multiple tasks such as feature selection, feature mining, and feature integration during training, resulting in poor classification results for small sample classification of hyperspectral images. To improve the classification of small samples, a sequential joint deep learning algorithm is proposed in this paper. (In this algorithm, the deep features of multiscale convolution under an attention mechanism are integrated by using Bidirectional Long Short Term Memory(Bi LSTM) and AML. First, we used principal component analysis (PCA) to reduce the dimensionality of the hyperspectral data and retain their key features. Second, the model uses an integrated attention mechanism to distribute the probability weight of the key input feature. Third, the model uses multiscale convolution to mine features after the distribution weight to obtain deep features. Fourth, the model uses bidirectional long short term memory (Bi LSTM) to integrate the convolution results at different scales. Finally, the softmax classifier is used to complete the classification of multiclass hyperspectral remote sensing images. Experiments were carried out on three public hyperspectral data sets, and the results proved that our proposed AML algorithm is effective, thus demonstrating powerful performance in the prediction of hyperspectral images (HSIs) of small samples.

title: Learning to Pay Attention on Spectral Domain: A Spectral Attention Module Based Convolutional Network for Hyperspectral Image Classification
abstract: Over the past few years, hyperspectral image classification using convolutional neural networks (CNNs) has progressed significantly. In spite of their effectiveness, given that hyperspectral images are of high dimensionality, CNNs can be hindered by their modeling of all spectral bands with the same weight, as probably not all bands are equally informative and predictive. Moreover, the usage of useless spectral bands in CNNs may even introduce noises and weaken the performance of networks. For the sake of boosting the representational capacity of CNNs for spectral spatial hyperspectral data classification, in this work, we improve networks by discriminating the significance of different spectral bands. We design a network unit, which is termed as the spectral attention module, that makes use of a gating mechanism to adaptively recalibrate spectral bands by selectively emphasizing informative bands and suppressing less useful ones. We theoretically analyze and discuss why such a spectral attention module helps in a CNN for hyperspectral image classification. We demonstrate using extensive experiments that in comparison with state of the art approaches, the spectral attention module based convolutional networks are able to offer competitive results. Furthermore, this work sheds light on how a CNN interacts with spectral bands for the purpose of classification.

title: Hyperspectral Image Classification
abstract: Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images. In the field of remote sensing, HSI classification has been an established research topic, and herein, the inherent primary challenges are (i) curse of dimensionality and (ii) insufficient samples pool during training. Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel. This chapter discusses the recent progress in the classification of HS images in the aspects of Kernel based methods, supervised and unsupervised classifiers, classification based on sparse representation, and spectral spatial classification. Further, the classification methods based on machine learning and the future directions are discussed.

title: Plant disease identification using explainable 3D deep learning on hyperspectral images
abstract: BackgroundHyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economically important disease, charcoal rot, which is a soil borne fungal disease that affects the yield of soybean crops worldwide.ResultsBased on hyperspectral imaging of inoculated and mock inoculated stem images, our 3D DCNN has a classification accuracy of 95.73% and an infected class F1 score of 0.87. Using the concept of a saliency map, we visualize the most sensitive pixel locations, and show that the spatial regions with visible disease symptoms are overwhelmingly chosen by the model for classification. We also find that the most sensitive wavelengths used by the model for classification are in the near infrared region (NIR) which is also the commonly used spectral range for determining the vegetative health of a plant.ConclusionThe use of an explainable deep learning model not only provides high accuracy, but also provides physiological insight into model predictions, thus generating confidence in model predictions. These explained predictions lend themselves for eventual use in precision agriculture and research application using automated phenotyping platforms.

title: Hyperspectral fruit and vegetable classification using convolutional neural networks
abstract: The classification of different types of fruits and vegetables is a difficult task, since many types are quite similar in color and shape. In this study, we show an easy way to classify hyperspectral images with state of the art convolutional neural networks pre trained for RGB image data. A small, custom dataset of hyperspectral images was recorded from staged but realistic scenes. With this dataset, an ImageNet pre trained convolutional neural network was fine tuned to obtain a classifier. An additional data compression layer has been added to be able to classify the hyperspectral images with the RGB pre trained network. To isolate the benefit of increased spectral resolution for the classification, the same analysis was also performed with pseudo RGB images calculated from the hyperspectral images. The results show that the hyperspectral image data increases the average classification accuracy from 88.15 to 92.23 The approach can easily be extended to other applications.

title: Deep Learning for Classification of Hyperspectral Data: A Comparative Review
abstract: In recent years, deep learning techniques revolutionized the way remote sensing data are processed. The classification of hyperspectral data is no exception to the rule, but it has intrinsic specificities that make the application of deep learning less straightforward than with other optical data. This article presents the state of the art of previous machine learning approaches, reviews the various deeplearning approaches currently proposed for hyperspectral classification, and identifies the problems and difficulties that arise in the implementation of deep neural networks for this task. In particular, the issues of spatial and spectral resolution, data volume, and transfer of models from multimedia images to hyperspectral data are addressed. Additionally, a comparative study of various families of network architectures is provided, and a software toolbox is publicly released to allow experimenting with these methods (https:/github.com/nshaud/DeepHyperX) This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deeplearning techniques to their own data set.

title: Unsupervised Spatial Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification
abstract: Feature learning technologies using convolutional neural networks (CNNs) have shown superior performance over traditional hand crafted feature extraction algorithms. However, a large number of labeled samples are generally required for CNN to learn effective features under classification task, which are hard to be obtained for hyperspectral remote sensing images. Therefore, in this paper, an unsupervised spatial spectral feature learning strategy is proposed for hyperspectral images using 3 Dimensional (3D) convolutional autoencoder (3D CAE) The proposed 3D CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial spectral structure information for feature extraction. A companion 3D convolutional decoder network is also designed to reconstruct the input patterns to the proposed 3D CAE, by which all the parameters involved in the network can be trained without labeled training samples. As a result, effective features are learned in an unsupervised mode that label information of pixels is not required. Experimental results on several benchmark hyperspectral data sets have demonstrated that our proposed 3D CAE is very effective in extracting spatial spectral features and outperforms not only traditional unsupervised feature extraction algorithms but also many supervised feature extraction algorithms in classification application.

title: Simultaneous Spectral Spatial Feature Selection and Extraction for Hyperspectral Images
abstract: In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g. the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e. the simultaneous spectral spatial feature selection and extraction algorithm, for hyperspectral images spectral spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.