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- 实验方案
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Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques
摘要： Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi’ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa = 0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa = 0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa = 0.9253) and 96.46% (Kappa = 0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species.
关键词： machine learning,waveband selection,mangrove species classification,close-range hyperspectral imaging,field hyperspectral measurement
更新于2025-09-23 15:23:52
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Doubly Q-switched Tm:YAP laser with g-C3N4 saturable absorber and AOM
摘要： Two-dimensional (2D) graphitic carbon nitride (g-C3N4) was fabricated as saturable absorber (SA) and the nonlinear optical absorption properties were measured. A laser-diode (LD) pumped doubly Q-switched Tm-doped yttrium-aluminium perovskite YAlO3 (Tm:YAP) laser at 2 μm with g-C3N4 and acousto-optic modulator (AOM) is presented for the ?rst time to the best of our knowledge. Under an absorbed pump power of 5.34 W, the minimum pulse width of 239 ns and the maximum peak power of 1146 W were obtained by the doubly Q-switched laser. In comparison with the singly Q-switched laser using g-C3N4 SA or AOM, the dual-loss-modulated Q-switched laser could generate shorter pulse width and higher peak power. The maximum pulse width compression ratio was 4.48 and the highest peak power enhancement factor was 241. The experimental results indicated that 2D g-C3N4 SA is potential in Q-switched laser at 2 μm and the doubly Q-switched technology is a useful way to compress the pulse width and improve the peak power.
关键词： Pulse width,Doubly Q-switch,g-C3N4,Peak power,2 μm waveband
更新于2025-09-19 17:13:59
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Two-mode multiplexer based on the multilayer Si-SiN platform for 2μm waveband
摘要： We experimentally demonstrated a two-mode multiplexer in the multi-layer Si-SiN platform for 2um waveband. The insertion loss of the mode multiplexer link is less than 2.2 dB across the wavelength 1945 nm-1985 nm.
关键词： Mode Multiplexer,Mid Infra-red,2um waveband,Silicon Photonics
更新于2025-09-16 10:30:52
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Enhancing the performance of Multiple Endmember Spectral Mixture Analysis (MESMA) for urban land cover mapping using airborne lidar data and band selection
摘要： Multiple Endmember Spectral Mixture Analysis (MESMA) is a widely applied tool to retrieve spatially explicit information on urban land cover from both hyperspectral and multispectral data, but is still prone to misclassification errors when faced with high inter-class similarity, typical of the complex urban environment. In this study we assessed multiple ways to minimize spectral confusion using airborne lidar data as an additional data source and spectral feature selection. Several approaches were tested using simulated hyperspectral data and two case studies in the city of Brussels, Belgium, one based on hyperspectral (APEX) data and one on multispectral (Sentinel-2) data. We found that the implementation of height distribution information (1) as an endmember model selection tool and (2) as a basis for additional fraction constraints at the individual pixel scale, significantly reduced spectral confusion between spectrally similar, but structurally different land cover classes (on average by 80% for the APEX case). This had a net positive effect on subpixel fraction estimations (average R2 increased from 0.34 to 0.80 and from 0.23 to 0.63 for APEX and Sentinel-2, respectively) and pixel classification accuracies (kappa increased from 0.38 to 0.6 for the APEX case). When applied to fine spatial resolution data containing many single-class pixels, endmember model selection based on height information resulted in the additional benefit of lowering computation times by 85%. Spectral feature selection successfully discarded redundant spectral information (on average retaining only 19 out of 218 bands), thereby further lowering processing times by 50%, without affecting accuracies. Despite these significant improvements, spectral confusion remained an issue between classes showing no distinction in height information, particularly pavement and soil. Future research should therefore focus on integrating the proposed approach with advanced endmember detection and selection algorithms, along with exploring innovative ways of highlighting small spectral differences using spectral transformations. The algorithm we propose constitutes a viable approach for mapping of structurally diverse ecosystems, such as urban environments, at multiple spatial scales and with varying level of thematic detail.
关键词： Spectral unmixing,Digital surface model,Endmember model constraint,Fusion,Feature selection,Fraction constraint,Waveband adaptive spectral mixture analysis
更新于2025-09-09 09:28:46