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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
Quad-PolSAR data classification using modified random forest algorithms to map halophytic plants in arid areas
No full textJointly Informative and Manifold Structure Representative Sampling Based Active Learning for Remote Sensing Image Classification
No full textInformation fusion for urban road extraction from VHR optical satellite images
No full textJoint Urban Remote Sensing Event (JURSE), 2015, Lausanne, SwitzerlandDepartment of Land Surveying and Geo-Informatic
Multiscale Morphological Compressed Change Vector Analysis for Unsupervised Multiple Change Detection
No full textA novel multiscale morphological compressed change vector analysis (M2C2VA) method is proposed to address the multiple-change detection problem (i.e., identifying different classes of changes) in bitemporal remote sensing images. The proposed approach contributes to extend the state-of-the-art spectrum-based compressed change vector analysis (C2VA) method by jointly analyzing the spectral-spatial change information. In greater details, reconstructed spectral change vector features are built according to a morphological analysis. Thus more geometrical details of change classes are preserved while exploiting the interaction of a pixel with its adjacent regions. Two multiscale ensemble strategies, i.e., data level and decision level fusion, are designed to integrate the change information represented at different scales of features or to combine the change detection results obtained by the detector at different scales, respectively. A detailed scale sensitivity analysis is carried out to investigate its impacts on the performance of the proposed method. The proposed method is designed in an unsupervised fashion without requiring any ground reference data. The proposed M2C2VA is tested on one simulated and three real bitemporal remote sensing images showing its properties in terms of different image size and spatial resolution. Experimental results confirm its effectiveness
New Scheme for Impervious Surface Area Mapping From SAR Images With Auxiliary User-Generated Content
No full textA spectral-spatial multiscale approach for unsupervised multiple change detection
No full textA novel spectral-spatial joint multiscale approach is developed to address the multi-class change detection problem in bitemporal multispectral remote sensing images. The proposed approach is based on a multiscale morphological compressed change vector analysis (M2C2VA), which extend the state-of-the-art spectrum-based compressed change vector analysis (C2VA) while preserving more geometrical details of change targets. In particular, spectral change features are reconstructed according to the morphological analysis which exploiting the interaction of a pixel with its adjacent regions. Two multiscale ensemble strategies are proposed to integrate the change information represented at multiple scales in order to enhance the CD performance. The proposed approach is designed in an unsupervised fashion thus can be implemented without using ground reference data. A pair of real bitemporal remote sensing images is used to test the proposed approach and the obtained experimental results confirm its effectiveness
Geodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transfer
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