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Hyperspectral image (HSI) classification is an important research topic in detailed analysis of the Earth’s surface. However, the performance of the classification is often hampered by the high-dimensionality features and limited training samples of the HSIs which has fostered research about semi-supervised learning (SSL). In this paper, we propose a shape adaptive neighborhood information (SANI) based SSL (SANI-SSL) method that takes full advantage of the adaptive spatial information to select valuable unlabeled samples in order to improve the classification ability. The improvement of the classification mainly relies on two aspects: (1) the improvement of the feature discriminability, which is accomplished by exploiting spectral-spatial information, and (2) the improvement of the training samples’ representativeness which is accomplished by exploiting the SANI for both labeled and unlabeled samples. First, the SANI of labeled samples is extracted, and the breaking ties (BT) method is used in order to select valuable unlabeled samples from the labeled samples’ neighborhood. Second, the SANI of unlabeled samples are also used to find more valuable samples, with the classifier combination method being used as a strategy to ensure confidence and the adaptive interval method used as a strategy to ensure informativeness. The experimental comparison results tested on three benchmark HSI datasets have demonstrated the significantly superior performance of our proposed method.

Large-scale multispectral remote sensing data are often unavailable for some practical applications. Spectral resolution enhancement for large-scale multispectral remote sensing images by incorporating small-scale hyperspectral remote sensing images is an alternative way to generate remote sensing images with both large spatial range and high spectral resolution. This paper proposes an improved spectral resolution enhancement method (ISREM) using spectral matrix and weighting the spectral angle of the transformation matrix. ISREM is tested in a typical area of the Three-River Headwaters region (TRHR) to produce a synthetic hyperspectral image (HSI). Two existing spectral resolution enhancement methods, the color resolution improvement software package (CRISP) and spectral resolution enhancement method (SREM), are adopted to compare with ISREM. To further test the practicality of the synthetic HSIs generated by the ISREM, CRISP and SREM, they are used to estimate the coverage of native plant species (NPS) using support vector machines (SVM) and random forest (RF) regressions. The experimental results are as follows. (1) For the Pearson correlation coefficient between the synthetic HSI and original image, ISREM yielded the largest value of 0.9582, followed by CRISP and SREM with values of 0.9480 and 0.9514. For spectral similarity, the HSI generated by the ISREM was the closest to the original reference HSI in the spectral curve. It also showed the best cumulative performance with the use of the three quality evaluation indexes. (2) The identification accuracies of native plant species were 93.51%, 90.91%, 89.61% and 89.61% using generated HSIs and original multispectral image (MSI) within a threshold of 20%, respectively. Compared with original MSI, the synthetic HSI showed better ability to identify NPS in the study area, which further illustrated the effectiveness of the ISREM. (3) The ISREM can reduce the strict requirement of pure pixels and maintain the quality of synthetic HSI by spectral angle weighting. Hence, the proposed ISREM outperforms the existing CRISP and SREM methods in image spectral resolution enhancement of multispectral remote sensing images.

Hydrological models play an essential role in data assimilation (DA) systems. However, it is a challenging task to acquire the distributed hydrological model parameters that affect the accuracy of the simulations at a grid scale. Remote sensing data provide an ideal observation for DA to estimate parameters and state variables. In this study, a special assimilation scheme was proposed to jointly estimate parameters and soil moisture (SM) by assimilating brightness temperature (TB) from the Soil Moisture and Ocean Salinity (SMOS) mission. Variable infiltration capacity (VIC) hydrological model and L-band microwave emission of the biosphere model (L-MEB) are coupled as model and observation operators, respectively. The scheme combines two stages of estimators, one for the static model parameters and the other for the dynamic state variables. The estimators approximate the posterior probability distribution of an unknown target through sequential Monte Carlo (SMC) sampling. Markov chain Monte Carlo (MCMC) and immune evolution strategy are embedded in both stages to solve particle impoverishment problems. To evaluate the effectiveness of the scheme, the estimated SM sets are compared with in-situ observations and SMOS products in Maqu on the Tibetan Plateau. Specifically, the root mean square error decreased from 0.126 to 0.087 m3m−3 for surface SM, with a slight impact on the root zone. The temporal correlation between DA results and in-situ measurements increased to 0.808 and 0.755 for surface SM (+0.057) and root zone SM (+0.040), respectively. The results demonstrate that assimilating TB has tremendous potential as an approach to improve the estimation of distributed model parameters and SMs of surface and root zone at a grid scale, and the immune evolution strategy is effective for increasing the accuracy of approximation in sampling.