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.

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.

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.

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.