Approaches for Hyperspectral Image Classification-Detailed Review (original) (raw)

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.

SOFT COMPUTING APPROACHES FOR HYPERSPECTRAL IMAGE CLASSIFICATION

ICTACT Journal on Soft Computing, 2020

Hyperspectral image classification is one of the most emerging form of image classification. It is able to convey information about an image in a more detailed way as compared to RGB or multispectral data. When spectral measurement is performed using hundreds of narrow contiguous wavelength intervals, the resulting image is called a hyperspectral image. Spectral signature of thousands of materials have been measured in the laboratory and gathered into libraries. Library signatures are used as the basis for identification of materials in Hyperspectral Image (HSI) data. We analyze the spectral signature of the image to extract information. In HSI, each pixel is in fact a high dimensional vector typically containing reflectance measurement from hundreds of continuous narrow band spectral channels (FWHM between 2 and 20) and 400-2500 nm wavelength range. The range of spectrum in HSI data extends beyond the visible range. Hyperspectral data processing comes with many stages such as pre-processing, feature reduction, classification and followed by target detection. Various machine learning and deep learning algorithms have been used to classify HSI data where few of them are Support Vector Machine, Convolutional Neural Network, random forest, SSRN, etc. HSI is being used in variety of fields such as agriculture, mining, food quality, soil types, defense etc.

Pixel-Based Classification of Hyperspectral Images Using Convolutional Neural Networks

PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

The recent progress in geographical informationsystems, remote sensing (RS) and data analytics enables us to acquire and process large amount of Earth observation data. Convolutional neural networks (CNN) are being used frequently in classification of multi-dimensional images with high accuracy. In this paper, we test CNNs for the classification of hyperspectral RS data. Our proposed CNN is a multi-layered neural network architecture, which is tailored to classify objects based on pixel-wise spatial information using spectral bands of hyperspectral imagery (HSI). We use benchmark satellite imagery in four different HSI datasets for classification using the proposed architecture. Our results are compared with support vector machine (SVM) and extreme learning machine (ELM) algorithms, which are frequently usedtechniques of machine learning in RS data classification. Moreover, we also provide a comparison with the state-of-the-art CNN approaches, which have been used for HSI classification. Our results show improvements of up to 6% on average over SVM and ELM while up to 4% improvement is observed in comparison with two recently proposed CNN architectures for HSI classification accuracy. On the other hand, the processing time of ourproposed CNN is also significantly lower. Keywords Hyperspectral data • Machine learning • Convolutional neural networks Zusammenfassung Pixelweise Klassifizierung von Hyperspektralszenen mit Convolutional Neural Networks. Der Fortschritt bei Geoinformationssystemen, Fernerkundung und Datenanalyse erlaubt uns die Gewinnung und Verarbeitung von umfangreichen Erdbeobachtungdaten. Convolutional Neural Networks (CNN) werden oft zur Klassifizierung von multidimensionalen hoch aufgelösten Bilddaten verwendet. In diesem Artikel untersuchen wir die Eignung von CNNs für die Klassifizierung von hyperspektralen Fernerkundungsdaten. Das von uns vorgeschlagene CNN besitzt die Struktur eines neuronalen Netzwerks mit mehreren Ebenen zur Objekt-Klassifizierung auf der Grundlage einer pixelweisen Auswertung der hyperspektralen Bilddaten. Zur Verifizierung unserer Klassifizierungsmethode benutzen wir vier verschiedene Datensätze, aufgenommen von Satellitenplattformen. Die Ergebnisse werden mit denen der Methoden Support Vector Machine (SVM) und Extreme Learning Machine (ELM), die beide bei automatischen Klassifizierungsverfahren der Fernerkundung weit verbreitet sind, verglichen. Darüber hinaus liefern wir einen Vergleich zu aktuellen Ansätzen der CNN. Unsere Ergebnisse zeigen eine Verbesserung der Klassifizierungsgenauigkeit von 6% gegenüber SVM und ELM sowie eine Verbesserung von 4% gegenüber kürzlich veröffentlichen CNN-Architekturen. Darüber hinaus ist unser Ansatz deutlich schneller.

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%.

Classification of Hyperspectral Images by Using Spectral Data and Fully Connected Neural Network

2022

It is observed that high classification performance is achieved for one- and two-dimensional signals by using deep learning methods. In this context, most researchers have tried to classify hyperspectral images by using deep learning methods and classification success over 90% has been achieved for these images. Deep neural networks (DNN) actually consist of two parts: i) Convolutional neural network (CNN) and ii) fully connected neural network (FCNN). While CNN determines the features, FCNN is used in classification. In classification of the hyperspectral images, it is observed that almost all of the researchers used 2D or 3D convolution filters on the spatial data beside spectral data (features). It is convenient to use convolution filters on images or time signals. In hyperspectral images, each pixel is represented by a signature vector which consists of individual features that are independent of each other. Since the order of the features in the vector can be changed, it doesn&...

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.

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.

Methodology for hyperspectral image classification using novel neural network

Algorithms for Multispectral and Hyperspectral Imagery III, 1997

A novel feed forward neural network is used to classify hyperspectral data from the AVIRIS sensor. The network applies an alternating direction singular value decomposition technique to achieve rapid training times (few seconds per class). Very few samples (10-12) are required for training. 100% accurate classification is obtained using test data sets. The methodology combines this rapid training neural network together with data reduction and maximal feature separation techniques such as principal component analysis and simultaneous diagonalization of covariance matrices, for rapid and accurate classification of large hyperspectral images. The results are compared to those of standard statistical classifiers.

Efficient Classification of Hyperspectral Data Using Deep Neural Network Model

Human-centric Computing and Information Sciences , 2022

In recent years, there has been tremendous progress in the classification of hyperspectral images (HSI) using a deep neural network. Due to the high complexity of spectral properties, limited ground truth samples, and extreme inhomogeneity of hyperspectral data, an efficient classification of HSI using deep convolutional neural networks remains difficult. As a result, the ever-increasing volume of data necessitates the effective categorization of remote sensor-based HSI utilizing advanced deep learning. Over time, certain deep learning models for learning to detect HSI have been developed, and many researchers have used convolutional neural networks (CNNs). Previous research on hyperspectral data categorization using deep learning models have faced the issue of performance degradation due to insufficient layer selection in the CNN, which is studied in this research. In our solution, we have proposed a CNN model for effectively classifying hyperspectral data using deep learning. We tested our method using land cover benchmark datasets and found that it outperforms the current state-of-the-art. When compared to conventional machine learning methods and baseline procedures, the results of this study reveal that using an upgraded CNN model significantly improves the performance (accuracy = 93.621, precision = 91.571, recall = 92, F1 score = 90.714).

Methodology for hyperspectral image classification using novel neural network

Algorithms for Multispectral and Hyperspectral Imagery III, 1997

A novel feed forward neural network is used to classify hyperspectral data from the AVIRIS sensor. The network applies an alternating direction singular value decomposition technique to achieve rapid training times (few seconds per class). Very few samples (10-12) are required for training. 100% accurate classification is obtained using test data sets. The methodology combines this rapid training neural network together with data reduction and maximal feature separation techniques such as principal component analysis and simultaneous diagonalization OF covariance matrices, for rapid and accurate classification of large hyperspectral images. The results are compared1 to those of standard statistical classifiers.