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Special section guest editorial:Advanced spectral analysis techniques and remote sensing applications
Full text linkSpecial section editors Sicong Liu, Francesca Bovolo, Claudio Persello, Danfeng Hong, and Alim Samat introduce the Special Section on Advanced Spectral Analysis Techniques and Remote Sensing Applications.
Spectral analysis technology represents a fundamental tool for the extraction of valuable information from spectral detection and imaging data. It can be utilized in various remote sensing applications. With the development of optical sensors, traditional spectral analysis methods may face challenges that arise due to the higher spectral resolution of multispectral to hyperspectral data, wider spectral range including ultraviolet, visible, and infrared wavelengths. Therefore, more sophisticated spectral analysis technology is urgently required. In recent decades, machine learning, especially deep learning techniques, have brought spectral analysis into the era of artificial intelligence (AI), enabling both qualitative and quantitative analysis in a more precise and robust way.
This special section aims to collect the latest developments on spectral analysis techniques and remote sensing applications. Nine excellent papers have been included in this special section, covering the following topics including: (1) spectral analysis for soil organic matter estimation; (2) spectral analysis for vegetation parameter inversion; (3) hyperspectral image super-resolution; (4) hyperspectral image classification; (5) spectral change detection.
Zhou et al. proposed an improved standard-sample calibration transfer method, in order to study the transferability of machine learning prediction models between different soil types. The results obtained by the improved model demonstrated higher accuracy of SOM prediction compared with the sample mixing method. Guo et al. used partial least squares regression (PLSR) and support vector machine regression (SVR) to establish a SOM estimation model for monitoring of soil nutrients in tensile fissures, which can provide reference for the rapid and accurate estimation of SOM in coal mining fissure zones.
Magalhães et al. compared the performance of various regression models based on Sentinel satellite images, with the aim of indirectly estimating the value of canopy water content (CWC) and equivalent water thickness (EWT) in maize more accurately. The superior performance of the AdaBoost regression (AR) model was validated in this analysis. Yasir et al. proposed a new index for estimating leaf water content based on multi-angular reflection. The effectiveness and superiority of this index were validated by 683 samples of different plant species. Jia et al. used machine learning to integrate in-situ hyperspectral data with Sentinel-2 MSI images to combine their complementary advantages, which effectively improves the accuracy of large-scale Chlorophyll-a (Chl-a) concentration estimation.
Xu et al. proposed a two-stream self-attention network (TSSA-Net) to capture global features from both multispectral and hyperspectral images. The network comprises two streams, each of which is designed to extract spatial and spectral abundance maps. The proposed method enables the generation of more effective hyperspectral image super-resolution results can be obtained.
Fang et al. proposed a novel approach, the regularized spatial-spectral transformer for domain adaptation (RSTDA), with the aim of effectively extracting spatial-spectral features from HSI data and enhancing the accuracy of cross-scene HSI classification. To this end, they adopted a smooth adversarial training strategy within the model. Atik and Atik applied explainable artificial intelligence (XAI) technology to optimal band selection in hyperspectral image classification. Their findings indicate that XAI-based methods were capable of identifying informative bands and demonstrated superior performance in subsequent tasks, as compared to other methods.
Bhattacharjee, Chakravortty, and Ghosh used the Bayesian change point detection method to identify nonlinear responses and abrupt changes in mangrove health, indicating that small environmental stresses lead to large eco-system changes over time. Results confirms that the Hurst
t
-statistics method identifies the same change points as the Bayesian approach
Unsupervised Multitemporal Spectral Unmixing for Detecting Multiple Changes in Hyperspectral Images
No full textThis paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectral–temporal variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to each pixel. The proposed approach has been validated on both simulated and real multitemporal HS data sets presenting multiple changes. Experimental results confirmed the effectiveness of the proposed method
A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges
No full textWe review both widely used methods and new techniques proposed in the recent literature. The basic concepts, categories, open issues, and challenges related to CD in HS images are discussed and analyzed in detail. Experimental results obtained using state-of-the-art approaches are shown, to illustrate relevant concepts and problems
Multitemporal spectral unmixing for change detection in hyperspectral images
No full textThis paper develops a novel multitemporal spectral unmixing (MSU) approach for addressing the challenging multiple-change detection problem in bi-temporal hyperspectral (HS) images. Differently from state-of-the-art techniques that mainly perform at a pixel level, the proposed MSU approach investigates the spectral-temporal variations at a subpixel level. A multitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted and employed for distinguishing change and no-change MT-EMs in the unmixing model. The CD problem is solved by analyzing the abundances of the unique change and no-change multitemporal endmembers and their contribution to each pixel. Experimental results obtained on multitemporal Hyperion HS images confirmed the effectiveness of the proposed method
Editorial Foreword to the Special Issue on Recent Advances in Multitemporal Remote-Sensing Data Processing
No full textThis special issue of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) contains 11 papers both from the extended outcomes of the Multitemp 2019 presented papers, and from the submissions by following a general Call-for-Papers of this special issue. These papers focus on the interesting and relevant topics in the multitemporal data analysis
Jointly Informative and Manifold Structure Representative Sampling Based Active Learning for Remote Sensing Image Classification
No full textQuad-PolSAR data classification using modified random forest algorithms to map halophytic plants in arid areas
No full textUnsupervised Change Detection in Multitemporal Remote Sensing Images
No full textRemote sensing satellites have a great potential to recurrently monitor the dynamic changes of the Earth's surface in a wide geographical area, and contribute substantially to our current understanding of the land-cover and land-use changes. This chapter focuses on the unsupervised change detection (CD) problem in multitemporal multispectral images. It investigates the spectral–spatial change representation for addressing the important multiclass CD problem. Depending on the purpose of unsupervised CD tasks, two main categories of methods are defined: binary change detection and multiclass change detection. Deep learning-based CD approaches have shown great potential in extracting more high-level deep features, which represents a popular direction in CD research. The chapter introduces a proposed multiscale morphological compressed change vector analysis method. Owing to the automatic and unsupervised nature, unsupervised CD always represents a very interesting and important CD research and application frontier
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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