Hyperspectral Image Classification: Potentials, Challenges, and Future Directions (original) (raw)
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Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely 2D Convolutional neural network (2D-CNN), 3D Convolutional neural network (3D-CNN), recurrent 2D Convolutional neural network (R-2D-CNN), and recurrent 3D Convolutional neural network (R-3D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3D-CNN and the R-2D-CNN deep learning models.
Deep Learning for Hyperspectral Image Classification: An Overview
IEEE Transactions on Geoscience and Remote Sensing, 2019
Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework which divides the corresponding works into spectralfeature networks, spatial-feature networks, and spectral-spatialfeature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.
A SURVEY: DEEP LEARNING CLASSIFIERS FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Journal of Theoretical and Applied Information Technology, 2021
Hyperspectral imaging (HSI) is a popular subject in remote sensing data processing because of the huge quantity of data included in these images, which enables for improved description and utilization of the surface of the earth by integrating abundant spectral and spatial data. However, due to the high dimensionality of HSI data and the limited number of labelled examples available, conducting HSI image classification poses significant technical and pragmatic hurdles. Approaches to HSI classification with deep learning have gained major successes in recent years as new deep learning algorithms emerge, giving unique prospects for hyperspectral image classification research and development. Initially, a quick introduction to standard deep learning (DL) models is provided, followed by a comparison of the performance of common DL based HSI approaches. Finally, the difficulties and future research prospects are explored.
Hyperspectral Satellite Image Classification using Deep Learning
TEST Engineering & Management, 2020
Hyperspectral images are utilized to provide adequate spectral information so as to acknowledge and differentiate spectrally distinctive materials. Optical analysis techniques are utilized to detect and identify the objects from a scale of images. Hyperspectral imaging technique is one among them. Hyperspectral image classification research is an intense field of study and an outsized number ofrecent approacheshave been developed to enhance the performance for specific applications that exploit both spatial and spectral image content. The goal of hyperspectral imaging is to obtain the spectrum for each and every pixel within the image of a scene, with the intent of detecting processes, identifying materials or finding objects. In this particular study, a strategy for the classification of Hyperspectral satellite images is asserted using deep learning framework. Thisframeworkinvolves inception module architecture containing 1x1, 3x3 and 5x5Convolutional layers which gives an overallclassification accuracy of 97.30%.
Hyperspectral Image Classification - Traditional to Deep Models: A Survey for Future Prospects
ArXiv, 2021
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state...
Classification of Hyperspectral Imagery using Deep Learning
The classification of Hyperspectral Imagery (HSI) has been a focal point in pattern recognition, leading to diverse research outcomes. This study explores spectral-spatial information for classification, incorporating Deep Learning methodologies, and employing various models such as 3D Convolutional Neural Network (3DCNN), Graph Convolutional Network and Convolutional Neural Network (GCN+CNN), Autoencoder, Hyperspectral Vision Transformer (HVT), EarthMapper Toolbox, and Spectralformer. Each model is tailored to capture distinctive features and relationships within hyperspectral data. A comprehensive comparison reveals variations in performance across different classification tasks, considering spatial and spectral aspects, computational efficiency, and dataset characteristics. The study aims to guide practitioners in selecting suitable models based on specific application requirements. Future endeavors include integrating graph attention modules with Transformer networks and leveraging existing models for transfer learning on multispectral data in target domains. The results and insights presented contribute to the advancement of hyperspectral image classification methodologies
Hyperspectral Image Classification
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.
Deep Learning Based Hyperspectral Image Classification: A Review For Future Enhancement
International journal of computing and digital system/International Journal of Computing and Digital Systems, 2024
The use of Hyperspectral Image(HSI) has become prevalent in many sectors due to its ability to identify detailed spectral information (i.e., relationships between the collected spectral data and the object in the HSI data) that cannot be obtained through ordinary imaging. Traditional RGB image classification approaches are insufficient for hyperspectral image classification(HSIC) because they struggle to capture the subtle spectral information that exists within hyperspectral data. In the past few years, the Deep Learning(DL) based model has become a very powerful and efficient non-linear feature extractor for a wide range of computer vision tasks. Furthermore, DL-based models are exempt from manual feature extraction. The use of this stimulus prompted the researchers to use a DL-based model for the classification of Hyperspectral Images, which yielded impressive results. This motivation inspired the researchers to develop a DL-based model for the classification of hyperspectral images, which performed well. Deeper networks might encounter vanishing gradient problems, making optimization more difficult. To address this issue, regularisation and architectural improvements are being implemented. One of the key issues is that the DL-based HSIC model requires a large number of training samples for training, which is an important concern with hyperspectral data due to the scarcity of public HSI datasets. This article provides an overview of deep learning for hyperspectral image classification and assesses the most recent methods. Among all studied methods SpectralNET offers significantly better performance, due to the utilization of wavelet transformation.
Classification of hyperspectral imagery with neural networks: comparison to conventional tools
EURASIP Journal on Advances in Signal Processing, 2014
Efficient exploitation of hyperspectral imagery is of great importance in remote sensing. Artificial intelligence approaches have been receiving favorable reviews for classification of hyperspectral data because the complexity of such data challenges the limitations of many conventional methods. Artificial neural networks (ANNs) were shown to outperform traditional classifiers in many situations. However, studies that use the full spectral dimensionality of hyperspectral images to classify a large number of surface covers are scarce if non-existent. We advocate the need for methods that can handle the full dimensionality and a large number of classes to retain the discovery potential and the ability to discriminate classes with subtle spectral differences. We demonstrate that such a method exists in the family of ANNs. We compare the maximum likelihood, Mahalonobis distance, minimum distance, spectral angle mapper, and a hybrid ANN classifier for real hyperspectral AVIRIS data, using the full spectral resolution to map 23 cover types and using a small training set. Rigorous evaluation of the classification accuracies shows that the ANN outperforms the other methods and achieves ≈90% accuracy on test data.
Approaches for Hyperspectral Image Classification-Detailed Review
International Journal of Soft Computing and Engineering (IJSCE), 2021
Hyperspectral Image (HSI) processing is the new advancement in image / signal processing field. The growth over the years is appreciable. The main reason behind the successful growth of the Hyperspectral imaging field is due to the enormous amount of spectral and spatial information that the imagery contains. The spectral band that the HSI which contains is also more in number. When an image is captured through the HSI cameras, it contains around 200-250 images of the same scene. Nowadays HSI is used extensively in the fields of environmental monitoring, Crop-Field monitoring, Classification, Identification, Remote sensing applications, Surveillance etc. The spectral and spatial information content present in Hyperspectral images are with high resolutions.Hyperspectral imaging has shown significant growth and widely used in most of the remote sensing applications due to its presence of information of a scene over hundreds of contiguous bands In. Hyperspectral Image Classification of materials is the critical application of HSI using Hyperspectral sensors. It collects hundreds of spectrum channels, where each channel consists of a sharp point of Electromagnetic Spectrum. The paper mainly focuses on Deep Learning techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector machines (SVM), K-Nearest Neighbour (KNN) for the accuracy in classification. Finally in the summary the current state-of-the-art scheme, a critical discussion after reviewing the research work by other professionals and organizing it into review-based paper, also implying about the present status on classification accuracy using neural networks is carried out.