Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

A hyperspectral detection model for permeability coefficient of debris flow fine-grained sediments, Southwestern China

Fine-grained sediments are Quaternary sediments with grain sizes of not more than 2 mm.They startfirst when meeting water,their stability is related to the initial water volume triggering debrisflow,and thus plays an important role in debrisflow hazards early warning.The permeability coefficient is the inter-controlled factor offine-grained sediment stability.However,there is no hyperspectral model for detecting thefine-grained sediment permeability coefficient in large areas,which seriously affects the progress of debrisflow hazards early warning.Therefore,it is of great significance to establish a hyperspectral detection model for the permeability coefficient offine-grained sediments.Taking Beichuan County,Southwestern China as the case,a permeability coefficient hyperspectral detection model was established.The results show that eight bands are sensitive to the permeability coefficient with correlation coefficient(R)of 0.6343.T-test on the model shows that P-a values for sensitive bands are all less than 0.05,indicating the established model has a good prediction ability with a precision of 85.83%.These sensitive bands also indicate the spectral characteristics of the permeability coefficient.Therefore,it provides a scientific basis forfine-grained sediment stability detection in large areas and lays a theoretical foundation for debrisflow hazards’early warning.

A New Conception of Image Texture and Remote Sensing Image Segmentation Based on Markov Random Field

The texture analysis is often discussed in image processing domain, but most methods are limited within gray-level image or color image, and the present conception of texture is defined mainly based on gray-level image of single band. One of the essential characters of remote sensing image is multidimensional or even high-dimensional, and the traditional texture conception cannot contain enough information for these. Therefore, it is necessary to pursuit a proper texture definition based on remote sensing images, which is the first discussion in this paper. This paper describes the mapping model of spectral vector in two-dimensional image space using Markov random field (MRF), establishes a texture model of multiband remote sensing image based on MRF, and analyzes the calculations of Gibbs potential energy and Gibbs parameters. Further, this paper also analyzes the limitations of the traditional Gibbs model, prefers a new Gibbs model avoiding estimation of parameters, and presents a new texture segmentation algorithm for hy-perspectral remote sensing image later.