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Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintaining the regional ecological environment. However, large-scale coal mining in recent years has considerably impacted the deposition condition of several lakes. Rapid and accurate extraction of lake information based on satellite images is crucial for developing protective measures against desertification. However, the spatial resolution of these images often leads to mixed pixels near water boundaries, affecting extraction precision. Traditional pixel unmixing methods mainly obtain water coverage information in a mixed pixel, making it difficult to accurately describe the spatial distribution. In this paper, the cellular automata (CA) model was adopted in order to realize lake information extraction at a sub-pixel level. A mining area in Shenmu City, Shaanxi Province, China is selected as the research region, using the image of Sentinel-2 as the data source and the high spatial resolution UAV image as the reference. First, water coverage of mixed pixels in the Sentinel-2 image was calculated with the dimidiate pixel model and the fully constrained least squares (FCLS) method. Second, the mixed pixels were subdivided to form the cellular space at a sub-pixel level and the transition rules are constructed based on the water coverage information and spatial correlation. Lastly, the process was implemented using Python and IDL, with the ArcGIS and ENVI software being used for validation. The experiments show that the CA model can improve the sub-pixel positioning accuracy for lake bodies in mixed pixel image and improve classification accuracy. The FCLS-CA model has a higher accuracy and is able to identify most water bodies in the study area, and is therefore suitable for desert lake monitoring in mining areas. [ABSTRACT FROM AUTHOR]

The influence of the detection direction on the quality of spectral data was not taken into account, which is catastrophic for some samples where the reflectivity direction was very different. To explore the directional characteristics of mixed pixels reflectivity, we used an ASD FieldSpec3 spectroradiometer to carry out multi-angle spectral measurement experiments in the laboratory, and the direction error model is established. As a result, the proportions of black area (PBA) and the observed azimuths (Φ) will affect the reflectivity of the mixed pixel in the visible light band. It is found that when the PBA is close to 0, the reflectivity distribution of mixed pixels is characterized by the reflected energy reflecting uniformly around the entire hemisphere space. When PBA is close to 1, there is a significant difference in the reflectivity of mixed pixels in 2π space. The direction error model better reflects the reflectivity changes caused by PBA and observed azimuth. The mean absolute error of the estimated reflectivity compared with the measured value is only 0.047. When the PBA is large, the estimation accuracy of the model is higher. When the PBA is small, and the observed azimuth is large, the accuracy of the model is slightly worse. The "two-block" mixed pixel is an ideal sample to satisfy the direction error model, while a dispersed "multi-block" mixed pixel is not applicable to the correction model. When conducting mixed pixel spectral measurement experiment, the influence of the detection azimuth on the spectral reflectivity should be fully considered, which is beneficial to improve the reliability of experiments on multi-angle spectral measurements of mixed pixels. [ABSTRACT FROM AUTHOR]

In most hyperspectral target detection applications, targets are usually small and require both spatial as well as spectral detection. Hyperspectral imaging facilitates target detection (TD) applications greatly, however, due to large spectral content, hyperspectral data requires dimensionality reduction (DR) which also leads to loss of target information both at full pixel and subpixel level. Literature reports many DR and TD algorithms in practice. Several studies have focussed on assessing the loss of target information in DR, however, not much work seems to have been done to assess loss of target information in full pixel and subpixel TD in hyperspectral data with and without DR. This paper seeks to study various combinations of DR techniques combined with full pixel and subpixel TD algorithms. The results indicate that in the case of full pixel targets, both DR and TD contribute to the loss of target information, however, there is more loss of target information in the case when DR precedes TD in comparison to a case where TD is applied without DR. In the case of subpixel TD, however, there appears to be loss of subpixel target information in the case where TD alone is performed in comparison to a case where DR precedes TD. [ABSTRACT FROM AUTHOR]

Oil spilled on the sea ice surface in the Bohai Sea of China is studied through the field measurements of the reflectance of a simulated sea ice-oil film mixed pixel. The reflection characteristics of sea ice and oil film are also analyzed. It is found that the mixed pixel of sea ice and oil film is a linear mixed pixel. The means of extracting sea ice pixels containing oil film is presented using a double-band ratio oil-film sea-ice index (DROSI) and a normalized difference oil-film sea-ice index (NDOSI) through the analysis of the reflectance curves of the sea iceoil film pixel for different ratios of oil film. The area proportion of the oil film in the sea ice-oil film pixel can be accurately estimated by the average reflectance of the band of 1 610-1 630 nm, and the volume of the spilled oil can be further estimated. The method of the sea ice-oil film pixel extraction and the models to estimate the proportion of oil film area in the sea ice-oil film pixel can be applied to the oil spill monitoring of the ice-covered area in the Bohai Sea using multispectral or hyperspectral remote sensing images in the shortwave infrared band (1 500-1 780 nm). [ABSTRACT FROM AUTHOR]

Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. [ABSTRACT FROM AUTHOR]

A linear regression and curve fitting method is used to study the mixed pixel of lotus leaf and water body under the experimental conditions. The marginal value of hyperspectral features of vegetation is discussed. Through an analysis of the regression result and fitted equation, a conclusion is reached as follows: Until the area ratio of the lotus leaf is 5.9 %, the vegetation reflection features cannot be observed even factually there are some lotus leaves in the mixed pixel; when it is 10.7 %, the reflection spectrum curve saturates and the water body reflection features cannot been detected. The result indicates that although the area ratio of the lotus changes the reflection features may not change. This research will help to understand the mixed pixel and the liner spectrum unmixing technique further. [ABSTRACT FROM AUTHOR]

Mixed pixel is a key issue in medium to coarse resolution remote sensing image, and it seriously restricts the remote sensing classification. This paper presents an Independent component analysis (ICA) algorithm based on the variational Bayesian (VB) methods, named VBICA, for spectral unmixing in multispectral remote sensing image. The model assumes that the mixed pixels to be separated are given as linear mixtures. The matrixes of linear mixtures are assumed to be unknown. In the Bayesian framework, the endmember and abundance have finally been achieved with Bayesian inference and approximate variational algorithm. The proposed method is evaluated and tested on a numerical simulative image from the noise resistance, area size, pixel purity, estimated number of endmembers and real multispectral remote sensing image of 100 × 100 pixels. Experimental results on simulated image demonstrated that compared to the Fast ICA algorithm, the proposed algorithm can give more accurate results, and the validity of the proposed algorithm is verified by the real multispectral remote sensing image of the similarity on spectral curves, average similarity and ground objects distribution maps. [ABSTRACT FROM AUTHOR]