A new subpixel mapping (SPM) method based on a super-resolution convolutional neural network (SRCNN) is proposed to generate subpixel land cover maps for hyperspectral images. The SRCNN is used to restore the image spatial resolution from a coarse input image, which is equivalent to interpolation. First, an efficient subpixel convolutional neural network, which is a state-of-the-art SRCNN, is utilized to calculate the subpixel soft class value via a transfer learning strategy. Then, a classifier is used to transform the subpixel soft class values to hard-classified land cover maps with the constraint of fraction images. Experiments on three different hyperspectral images demonstrate that the SPM accuracy of the proposed SRCNN-based method is significantly better than those of three traditional SPM methods. In addition, the SRCNN-based SPM method has a simplified calculation process, does not require training data, and is less time consuming. This article provides a new solution for SPM of hyperspectral images.

Drought is a frequent global phenomenon that has the most significant influence on agriculture. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. This study aims to monitor and assess monthly agricultural drought using SIF data with 0.05-degree spatial resolution. The scaled SIF index was calculated during the crop-growing season (March-October, 2000-2017) in agricultural areas of North China Plain (NCP), and the monthly agricultural drought spatial distribution and severity were mapped. Results indicated that NCP experienced mild to severe drought during the study period, the severe drought (proportion more than 50%) affected months including March (2000, 2001, 2003, 2005, 2006, 2010, 2011 and 2012), April (2000, 2001, 2003, 2010 and 2011), May (2000, 2001, 2002 and 2004), June (2000 and 2001), September (2002) and October (2001 and 2002). By statistics, the average drought areas decreased from 2000 to 2017 in NCP. For frequency analysis, the frequencies of mild, moderate and severe droughts were less than 0.4 in most areas of NCP, but severe drought frequency exceeded 0.6 in some areas. The monthly correlation analysis showed that the scaled SIF index had a significant positive correlation with precipitation and crop yield (wheat and corn); the maximum correlation coefficients (R) were 0.53 (September), 0.76 (May) and 0.77 (October). These results indicated that the scaled SIF index is suitable for region agricultural drought monitoring.

Although accurate training and initialization information is difficult to acquire, unsupervised hyperspectral subpixel mapping (SPM) without relying on this predefined information is an insufficiently addressed research issue. This letter presents a novel Bayesian approach for unsupervised SPM of hyperspectral imagery (HSI) based on the Markov random field (MRF) and a band-weighted discrete spectral mixture model (BDSMM), with the following key characteristics. First, this is an unsupervised approach that allows adjustment of abundance and endmember information adaptively for less relying on algorithm initialization. Second, this approach consists of the BDSMM for accommodating the noise heterogeneity and the hidden label field of subpixels in HSI. The BDSMM also integrates SPM into the spectral mixture analysis and allows enhanced SPM by fully exploring the endmember-abundance patterns in HSI. Third, the MRF and BDSMM are integrated into a Bayesian framework to use both the spatial and spectral information efficiently, and an expectation-maximization (EM) approach is designed to solve the model by iteratively estimating the endmembers and the label field. Experiments on both simulated and real HSI demonstrate that the proposed algorithm can yield better performance than traditional methods.

Although Bayesian methods have been very effective for spatial–spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced subpixel mapping (SPM) by simultaneously considering several key issues, i.e., endmember variability, the discrete nature of subpixel class labels, and the spatial information in HSI. Therefore, we propose a new Bayesian SPM method based on the discrete endmember variability mixture model (DEMM) and Markov random field (MRF), which has three main characteristics. First, DEMM allows the advanced SPM by completely accounting for the endmember–abundance patterns of each pixel to accommodate the endmember variability, the discrete hidden class label field of subpixels, while taking into account the noise heterogeneity effect. Second, the discrete class label fields modeled by MRF together with the DEMM, which can be integrated into a novel Bayesian model to better exploit the spatial contextual and spectral information. Third, the resulting Bayesian model can be efficiently solved by a designed expectation–maximization iteration, where E-step estimates the subpixel class label field using a simulated annealing algorithm and M-step estimates the endmembers for each pixel in HSI using the alternating non-negative least squares approach. The experimental results on three HSI datasets demonstrate that the proposed approach outperforms previously available SPM techniques.

For subpixel mapping (SPM), ensuring the operational efficiency of the algorithm and mitigating the effect of abundance errors often cannot be achieved simultaneously. To solve the problem, we propose a new SPM method based on the spatial adaptive attraction model (SAAM) and conditional random fields (CRFs). Firstly, the proposed SAAM obtains the spatial adaptive attraction value by adaptively adjusting the spatial attraction value obtained using the traditional spatial attraction model, thereby turning the display form of the abundance constraints in the SPM into an implicit form for expression, to perform the physical significance of the abundance constraints with the relative size of the attraction value of each subpixel. Secondly, the spatial adaptive attraction value of the implicitly represented abundance constraints and the local spatial smoothing prior are modelled in the CRFs, and the model makes full use of the spatial information in the label field while considering the abundance constraint. Thirdly, Graph-cut is used to optimize the model, make the proposed SPM can not only guarantee the operational efficiency, but also extinguish the influence of abundance error and decrease the noise artefact on the results of SPM. Experiments on three remote sensing images show that the proposed SPM accuracy is considerably better than the previously available SPM methods and is the least time-consuming. This study provides a new solution for the SPM of remote-sensing images.

Unsupervised subpixel mapping (SPM) of hyperspectral image (HSI) is a challenging task due to the difficulties to integrate different prior information and model constraints into a coherent framework. This paper presents a Bayesian neural network for unsupervised HSI SPM, which has the following characteristics. First, the deep image prior (DIP) achieved by a fully convolutional neural network (FCNN) is used to model the spatial correlation efficiently and adaptively in the subpixel label domain. Second, a discrete spectral mixture model (DSMM) is designed to leverage the forward model for enhanced SPM. Third, an auto-encoder architecture is designed to integrate the FCNN and the DSMM to allow efficient unsupervised representational learning using both data and knowledge. Fourth, an expectation-maximization approach is designed to solve the resulting maximum a posteriori problem, where a purified means approach extracts endmembers, and the gradient descent approach updates FCNN parameters for subpixel label estimation. Comparative experiments on both real and simulated HSIs demonstrate that the proposed method outperforms other state-of-the-art methods in terms of both numerical accuracies and visual subpixel mapping results.