Optimization of desert lake information extraction from remote sensing images using cellular automata

Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintain-ing 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 monitor-ing in mining areas.

Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data

MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.

Characteristics of the sea ice reflectance spectrum polluted by oil spills based on field experiments in the Bohai Sea

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）.

A new approach based on orthogonal bases of data space to decomposition of mixed pixels for hyperspectral imagery

A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest norm. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms, such as N-FINDR, that it generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which the traditional simplex-based algorithms cannot do by themselves. Experimental results of both artificial simulated images and practical remote sensing images demonstrate the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.