Baofeng Guo

Soil organic carbon (SOC) content is a critical parameter for evaluating soil health. However, high redundancy and invalid information in soil hyperspectral data can reduce the accuracy and stability of SOC prediction models. This study developed a global partial least squares regression (PLSR) model and a local PLSR model for agricultural soils in the LUCAS 2015 database. Some variable selection methods were combined with the regression models and their effects on prediction accuracy were explored. In addition, when the genetic algorithm is utilized for spectral feature selection, we obtained a more representative spectral subset through a novel coding approach. The results illustrated that the best SOC estimation accuracy was achieved by the local PLSR combined with a coding-improved genetic algorithm (GA), with R ² of 0.71, RMSEP of 5.7 g kg ^-1 , and RPD of 1.87. This study demonstrates that appropriate spectral band selection only slightly enhances the model performance of both global and local regressions, as PLSR models using the full spectrum show similar performance. Local PLSR models consistently outperform global ones using full spectrum or variable selection algorithms.

Supervised hyperspectral classification is one of the important applications in hyperspectral remote sensing. Currently, the enormous complexity of the hyperspectral data makes the hyperspectral feature extraction a very challenging job. In this letter, we propose a feature extraction approach by developing a two-dimensional compact variational mode decomposition (2D-C-VMD) for the supervised hyperspectral images (HSIs) classification. Specifically, the HSIs is first decomposed into a series of mode components using 2D-C-VMD. Then for the subsequent supervised classification, a collection of feature samples are created by elaborating the collected pattern components. Finally, a support vector machine is adopted as the classifier to validate the aforementioned feature extraction algorithm. According to experimental findings, this approach obtained a high level of classification accuracy with a compact feature extraction process and the time for feature extraction is also shortened.

Hyperspectral unmixing (HU) is a technique for estimating a set of pure source signals (end members) and their proportions (abundances) from each pixel of the hyperspectral image. Non-negative matrix factorization (NMF) can decompose the observation matrix into the product of two non-negative matrices simultaneously and can be used in HU. Unfortunately, a limitation of many traditional NMF-based methods, i.e., the non-convexity of the objective function, may lead to a sub-optimal solution. Thus, we put forward a new unmixing method based on NMF under smoothing and sparse constraints to obtain a better solution. First, considering the sparseness of the abundance matrix, a weight sparse regularization is introduced into the NMF model to ensure the sparseness of the abundance matrix. Second, according to the similarity prior of the same feature in the adjacent pixels, a Total Variation regularization is further added to the NMF model to improve the smoothness of the abundance map. Finally, the signatures of each end member are modified smoothly in spectral space. Moreover, it is noticed that discontinuities may emerge due to the removal of noisy bands. Therefore, the spectral data are piecewise smooth in spectral space. Then, in this paper, a piecewise smoothness constraint is further applied to each column of the end-member matrix. Experiments are conducted to evaluate the effectiveness of the proposed method based on two different datasets, including a synthetic dataset and the real-life Cuprite dataset, respectively. Experimental results show that the proposed method outperforms several state-of-the-art HU methods. In the Cuprite hyperspectral dataset, the proposed method’s Spectral Angle Distance is 0.1694, compared to the TV-RSNMF method’s 0.1703, L_1/2NMF method’s 0.1925, and VCA-FCLS method’s 0.1872.

To analyse and compare hyperspectral signatures, features extraction and matching are two key issues. In this letter, hyperspectral absorption features and the corresponding matching algorithm are discussed. First, an absorption detection method is applied to catch all necessary spectral absorptions with improved reliability. Then, a weighted Hamming distance is proposed to match the binary absorption‐features. Next, an elastic matching scheme is designed to classify the hyperspectral data. Experiments of classification are carried out on six classes of vegetation from the Salinas data‐set. Results show that the proposed method not only increased the overall classification accuracy to 73.13% from back propagation neural network's 71.86% and support vector machine's 73.06%, but also improved the error distributions among different classes.

One open issue of target detection for synthetic aperture radar (SAR) images is the capture effect from the clutter edge and the interfering outliers, including surrounding targets in the multitarget environment, sidelobes, and ghosts. To address this issue, a superpixel-level constant false-alarm rate (CFAR) detector is proposed based on the truncated Gamma statistics for the multilook intensity SAR data. Superpixel segmentation serves as a preprocessing procedure to divide the SAR image into meaningful patches. By automatic clutter truncation in the superpixel-level background clutter window, the real clutter samples are preserved. The experimental results with real SAR images demonstrate that the proposed method achieves better goodness-of-fit performance for the real clutter background with outlier exclusion, yielding a higher target detection rate in the multitarget environments.

In this paper, we propose to extract spectral absorptions as the discriminative features to classify hyperspectral imagery. Different from previous researches that mainly take hyperspectral curves as high-dimensional inputs, we analyze hyperspectral data more from its physical and chemical origins. In the proposed approach, the discriminatory information, which is characterized by the observed materials’ constituents, is extracted as a group of absorption features. First, the original hyperspectral spectra are transformed to a normalized spectra, in which a modified continuum removal algorithm is adopted to highlight all spectral valleys. Next, a standard peak detection method is applied to the continuum-removed spectra, and all effective absorptions are found as the candidate features. Then, to obtain the most informative absorptions to classification, a novel mutual-information based feature selection method is used to search for the key absorption spectra. Finally, we put forward a matching algorithm to classify the absorption features using the multi-label learning. To testify the proposed method, both laboratory and remotely sensed hyperspectral data are used to evaluate the classification performance. Experimental results show that the proposed method achieves competitive classification accuracy against the state-of-the-art methods, but with an advantage of more compact feature representation.

To better classify remotely sensed hyperspectral imagery, we study hyperspectral signatures from a different view, in which the discriminatory information is divided as reflectance features and absorption features, respectively. Based on this categorization, we put forward an information fusion approach, where the reflectance features and the absorption features are processed by different algorithms. Their outputs are considered as initial decisions, and then fused by a decision-level algorithm, where the entropy of the classification output is used to balance between the two decisions. The final decision is reached by modifying the decision of the reflectance features via the results of the absorption features. Simulations are carried out to assess the classification performance based on two AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) hyperspectral datasets. The results show that the proposed method increases the classification accuracy against the state-of-the-art methods.

When the inverse synthetic aperture radar (ISAR) system images the target, the radar emits a wide band linear frequency modulation (LFM) signal. The received echo is modulated by the radar transmitting channel and receiving channel, which is no longer an ideal LFM signal. Then the echo is mismatched with the ideal matched filter, which results in the range profile defocusing. In order to improvSe the imaging quality, a channel calibration method for the ISAR imaging system is proposed in this paper. Firstly, the radar illuminates nearby fixed-point target and receives the baseband echo. Secondly, construct the channel calibration coefficient with an ideal matched filter to eliminate the adverse effect on imaging caused by the non-ideal characteristics of transmitting channel and receiving channel. Simulation experiments verified the effectiveness of the proposed method.

When the radar detects the maneuvering target, there is a high-order term in the echo phase. When the coherent integration is carried out, the phenomenon of migration through Doppler cells occurs, which affects the target detection performance. Aiming at the detection problem of maneuvering target, a detection algorithm based on Matched Fourier Transform (MFT) is proposed. By constructing a basis function that is consistent with the target echo phase, the algorithm eliminated the problem of Doppler migration and can effectively estimate the motion parameters such as speed and acceleration of the target. Simulation experiments verified the effectiveness of the proposed algorithm.