Hyperspectral Pansharpening in the Reflective Domain with a Second Panchromatic Channel in the SWIR II Spectral Domain

Hyperspectral pansharpening methods in the reflective domain are limited by the large difference between the visible panchromatic (PAN) and hyperspectral (HS) spectral ranges, which notably leads to poor representation of the SWIR (1.0–2.5 μm) spectral domain. A novel instrument concept is proposed in this study, by introducing a second PAN channel in the SWIR II (2.0–2.5 μm) spectral domain. Two extended fusion methods are proposed to process both PAN channels, namely, Gain-2P and CONDOR-2P: the first one is an extended version of the Brovey transform, whereas the second one adds mixed pixel preprocessing steps to Gain-2P. By following an exhaustive performance-assessment protocol including global, refined, and local numerical analyses supplemented by supervised classification, we evaluated the updated methods on peri-urban and urban datasets. The results confirm the significant contribution of the second PAN channel (up to 45% of improvement for both datasets with the mean normalised gap in the reflective domain and 60% in the SWIR domain only) and reveal a clear advantage for CONDOR-2P (as compared with Gain-2P) regarding the peri-urban dataset.

NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

An accurate paddy rice map is crucial for ensuring food security, particularly for Southeast and Northeast Asia. MODIS satellite data are useful for mapping paddy rice at continental scales but have a mixed-pixel problem caused by the coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time series Sentinel-1 and MODIS data. We demonstrated the method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with available agricultural statistics at subnational levels and existing rice maps for some countries. The results demonstrated that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranged from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2=0.93) and Northeast China (R2=0.99). The spatial distribution of the 2017–2019 composite paddy rice map was consistent with that of the rice map from the International Rice Research Institute. The paddy rice planting area may have been underestimated in the region in which the flooding signal was not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.5281/zenodo.5645344 (Han et al., 2021a). Small examples can be found from the following DOI: https://doi.org/10.17632/cnc3tkbwcm.1 (Han et al., 2021b).

Multimedia Image Encryption Analysis Based on High-Dimensional Chaos Algorithm

With the development of network and multimedia technology, multimedia communication has attracted the attention of researchers. Image encryption has become an urgent need for secure multimedia communication. Compared with the traditional encryption system, encryption algorithms based on chaos are easier to implement, which makes them more suitable for large-scale data encryption. The calculation method of image encryption proposed in this paper is a combination of high-dimensional chaotic systems. This algorithm is mainly used for graph mapping and used the Lorenz system to expand and replace them one by one. Studies have shown that this calculation method causes mixed pixel values, good diffusion performance, and strong key performance with strong resistance. The pixel of the encrypted picture is distributed relatively random, and the characteristics of similar loudness are not relevant. It is proved through experiments that the above calculation methods have strong safety performance.

Early Classification Method for US Corn and Soybean by Incorporating MODIS-Estimated Phenological Data and Historical Classification Maps in Random-Forest Regression Algorithm

An early crop classification method is functionally required in a near-real-time crop-yield prediction system, especially for upland crops. This study proposes methods to estimate the mixed-pixel ratio of corn, soybean, and other classes within a low-resolution MODIS pixel by coupling MODIS-derived crop phenology information and the past Cropland Data Layer in a random-forest regression algorithm. Verification of the classification accuracy was conducted for the Midwestern United States. The following conclusions are drawn: The use of the random-forest algorithm is effective in estimating the mixed-pixel ratio, which leads to stable classification accuracy; the fusion of historical data and MODIS-derived crop phenology information provides much better crop classification accuracy than when these are used individually; and the input of a longer MODIS data period can improve classification accuracy, especially after day of year 279, because of improved estimation accuracy for the soybean emergence date.

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.

AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

An accurate paddy rice map is crucial for food security, particularly for Southeast and Northeast Asia. The MODIS satellite data is useful for mapping paddy rice at continental scales but has a problem of mixed-pixel due to coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time-series Sentinel-1 and MODIS data. We demonstrated the method in generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (AsiaRiceMap10m). The resultant paddy rice maps were compared with available agricultural statistics at subnational levels and existing rice maps for some countries. The results show that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranges from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2 = 0.93) and Northeast China (R2 = 0.99). The spatial distribution of the 2017–2019 composite paddy rice map is consistent with the rice map from the International Rice Research Institute. The paddy rice planting area may be underestimated in the region where the flooding signal is not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.17632/j34b3jsvr9.1 (Han et al., 2021a). Find small examples here (https://doi.org/10.17632/cnc3tkbwcm.1) (Han et al., 2021b).

Semantic feature extraction method for hyperspectral crop classification

<span>Hyperspectral imaging (HSI) is composed of several hundred of narrow bands (NB) with high spectral correlation and is widely used in crop classification; thus induces time and space complexity, resulting in high computational overhead and Hughes phenomenon in processing these images. Dimensional reduction technique such as band selection and feature extraction plays an important part in enhancing performance of hyperspectral image classification. However, existing method are not efficient when put forth in noisy and mixed pixel environment with dynamic illumination and climatic condition. Here the proposed Sematic Feature Representation based HSI (SFR-HSI) crop classification method first employ Image Fusion (IF) method for finding meaningful features from raw HSI spectrally. Second, to extract inherent features that keeps spatially meaningful representation of different crops by eliminating shading elements. Then, the meaningful feature set are used for training using Support vector machine (SVM). Experiment outcome shows proposed HSI crop classification model achieves much better accuracies and Kappa coefficient performance. </span>

Spatial Scale Effect of a Typical Polarized Remote Sensor on Detecting Ground Objects

For polarized remote sensors, the polarization images of ground objects acquired at different spatial scales will be different due to the spatial heterogeneity of the ground object targets and the limitation of imaging resolution. In this paper, the quantitative inversion problem of a typical polarized remote sensor at different spatial scales was studied. Firstly, the surface roughness of coatings was inversed based on the polarized bidirectional reflectance distribution function (pBRDF) model according to their polarization images at different distances. A linear-mixed pixel model was used to make a preliminary correction of the spatial scale effect. Secondly, the super-resolution image reconstruction of the polarization imager was realized based on the projection onto convex sets (POCS) method. Then, images with different resolutions at a fixed distance were obtained by utilizing this super-resolution image reconstruction method and the optimal spatial scale under the scene can be acquired by using information entropy as an evaluation indicator. Finally, the experimental results showed that the roughness inversion of coatings has the highest accuracy in the optimal spatial scale. It has been proved that our proposed method can provide a reliable way to reduce the spatial effect of the polarized remote sensor and to improve the inversion accuracy.

Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance

Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances. Recently, number of algorithms based on sparse nonnegative matrix factorization (NMF) have been widely used in hyperspectral unmixing with good performance. However, these sparse NMF algorithms only consider the correlation characteristics of abundance and usually just take the Euclidean structure of data into account, which can make the extracted endmembers become inaccurate. Therefore, with the aim of addressing this problem, we present a sparse NMF algorithm based on endmember independence and spatial weighted abundance in this paper. Firstly, it is assumed that the extracted endmembers should be independent from each other. Thus, by utilizing the autocorrelation matrix of endmembers, the constraint based on endmember independence is to be constructed in the model. In addition, two spatial weights for abundance by neighborhood pixels and correlation coefficient are proposed to make the estimated abundance smoother so as to further explore the underlying structure of hyperspectral data. The proposed algorithm not only considers the relevant characteristics of endmembers and abundances simultaneously, but also makes full use of the spatial-spectral information in the image, achieving a more desired unmixing performance. The experiment results on several data sets further verify the effectiveness of the proposed algorithm.