{"title":"Hyperspectral Image Classification","authors":"Rajesh Gogineni, A. Chaturvedi","doi":"10.5772/intechopen.88925","DOIUrl":"https://doi.org/10.5772/intechopen.88925","url":null,"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.","PeriodicalId":436109,"journal":{"name":"Processing and Analysis of Hyperspectral Data","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133877463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hyperspectral Endmember Extraction Techniques","authors":"K. Kale, Mahesh M. Solankar, D. B. Nalawade","doi":"10.5772/intechopen.88910","DOIUrl":"https://doi.org/10.5772/intechopen.88910","url":null,"abstract":"Hyperspectral data processing and analysis mainly plays a vital role in detection, identification, discrimination and estimation of earth surface materials. It involves atmospheric correction, dimensionality reduction, endmember extraction, spectral unmixing and classification phases. One of the ultimate aims of hyperspectral data processing and analysis is to achieve high classification accuracy. The classification accuracy of hyperspectral data most probably depends upon image-derived endmembers. Ideally, an endmember is defined as a spectrally unique, idealized and pure signature of a surface material. Extraction of consistent and desired endmember is one of the important criteria to achieve the high accuracy of hyperspectral data classification and spectral unmixing. Several methods, strategies and algorithms are proposed by various researchers to extract the endmembers from hyperspectral imagery. Most of these techniques and algorithms are significantly dependent on user-defined input parameters, and this issue is subjective because there is no standard specificity about these input parameters. This leads to inconsistencies in overall endmember extraction. To resolve the aforementioned problems, systematic, generic, robust and automated mechanism of endmember extraction is required. This chapter gives and highlights the generic approach of endmember extraction with popular algorithm limitations and challenges.","PeriodicalId":436109,"journal":{"name":"Processing and Analysis of Hyperspectral Data","volume":"13 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129127587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Use of Hyperspectral Remote Sensing to Estimate Water Quality","authors":"M. Mbuh","doi":"10.5772/intechopen.89222","DOIUrl":"https://doi.org/10.5772/intechopen.89222","url":null,"abstract":"Approximating and forecasting water variables like phosphorus, nitrogen, chlorophyll, dissolved organic matter, and turbidity are of supreme importance due to their strong influence on water resource quality. This chapter is aimed at showing the practicability of merging water quality observations from remote sensing with water quality modeling for efficient and effective monitoring of water quality. We examine the spatial dynamics of water quality with hyperspectral remote sensing and present approaches that can be used to estimate water quality using hyperspectral images. The methods presented here have been embraced because the blue-green and green algae peak wavelengths reflectance are close together and make their distinction more challenging. It has also been established that hyperspectral imagers permit an improved recognition of chlorophyll and hereafter algae, due to acquired narrow spectral bands between 450 nm and 600 nm . We start by describing the practical application of hyperspectral remote sensing data in water quality modeling. The surface inherent optical properties of absorption and backscatter-ing of chlorophyll a, colored dissolved organic matter (CDOM), and turbidity are estimated, and a detailed approach on analyzing ARCHER data for water quality estimation is presented.","PeriodicalId":436109,"journal":{"name":"Processing and Analysis of Hyperspectral Data","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134219721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hyperspectral Image Super-Resolution Using Optimization and DCNN-Based Methods","authors":"Xianhua Han","doi":"10.5772/intechopen.89243","DOIUrl":"https://doi.org/10.5772/intechopen.89243","url":null,"abstract":"Reconstructing a high-resolution (HR) hyperspectral (HS) image from the observed low-resolution (LR) hyperspectral image or a high-resolution multispectral (RGB) image obtained using the exiting imaging cameras is an important research topic for capturing comprehensive scene information in both spatial and spectral domains. The HR-HS hyperspectral image reconstruction mainly consists of two research strategies: optimization-based and the deep convolutional neural network-based learning methods. The optimization-based approaches estimate HR-HS image via minimizing the reconstruction errors of the available low-resolution hyperspectral and high-resolution multispectral images with different constrained prior knowledge such as representation sparsity, spectral physical properties, spatial smoothness, and so on. Recently, deep convolutional neural network (DCNN) has been applied to resolution enhancement of natural images and is proven to achieve promising performance. This chapter provides a comprehensive description of not only the conventional optimization-based methods but also the recently investigated DCNN-based learning methods for HS image super-resolution, which mainly include spectral reconstruction CNN and spatial and spectral fusion CNN. Experi-ment results on benchmark datasets have been shown for validating effectiveness of HS image super-resolution in both quantitative values and visual effect.","PeriodicalId":436109,"journal":{"name":"Processing and Analysis of Hyperspectral Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128839392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carlos Villaseñor, Javier Gomez-Avila, N. Arana-Daniel, A. Alanis, C. López-Franco
{"title":"Fast Chaotic Encryption for Hyperspectral Images","authors":"Carlos Villaseñor, Javier Gomez-Avila, N. Arana-Daniel, A. Alanis, C. López-Franco","doi":"10.5772/intechopen.88980","DOIUrl":"https://doi.org/10.5772/intechopen.88980","url":null,"abstract":"The information collected by hyperspectral images (HI) is essential in applications of remote sensing like object detection, geological process recognition, and identifying materials. However, HI information could be sensitive, and therefore, it should be protected. In this chapter, we show a parallel encryption algorithm specifically designed for HI. The algorithm uses multiple chaotic systems to produce a crossed multidimensional chaotic map for encrypting the image; the scheme takes advantage of the multidimensional nature of HI and is highly parallelizable, which leads to a time-efficient algorithm. We also show that the algorithm gets high-entropy ciphertext and is robust to ciphertext-only attacks.","PeriodicalId":436109,"journal":{"name":"Processing and Analysis of Hyperspectral Data","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124020810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"NIR Hyperspectral Imaging for Mapping of Moisture Content Distribution in Tea Buds during Dehydration","authors":"Keqiang Yu, Yanru Zhao, Xiaoli Li, Yong He","doi":"10.5772/INTECHOPEN.86095","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.86095","url":null,"abstract":"This work employed hyperspectral imaging technique to map the spatial distribution of moisture content (MC) in tea buds during dehydration. Hyperspectral images (874–1734 nm) of tea buds were acquired in six dehydrated periods (0, 3, 6, 9, 14 and 21 min) at 80°C. The spectral reflectance of tea buds were extracted from region of interests (ROIs) in the hyperspectral images. Competitive adaptive reweighted sampling (CARS) was used to select effective wavelengths (EWs) and ten representing the wavelengths were selected. The quantitative relationship between spectral reflectance and the measured MC values of tea buds was built using partial least square regression (PLSR) based on full spectra and EWs. The quantitative model established using EWs, which had a result of coefficient of correlation (R P ) of 0.941 and root mean square error of prediction (RMSEP) of 5.31%, was considered as the optimal model for mapping MC distribution. The optimal model was finally applied to predict the MC of each pixel within of the tea bud sample and built the MC distribution maps by utilization of a developed image processing procedure. Results demonstrated that the hyperspectral imaging technique has the potential of mapping the MC spatial distribution in tea buds in dehydrated process.","PeriodicalId":436109,"journal":{"name":"Processing and Analysis of Hyperspectral Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129799113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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