Evaluation and Intercomparison of Topographic Correction Methods Based on Landsat Images and Simulated Data

Topographic effects in medium and high spatial resolution remote sensing images greatly limit the application of quantitative parameter retrieval and analysis in mountainous areas. Many topographic correction methods have been proposed to reduce such effects. Comparative analyses on topographic correction algorithms have been carried out, some of which drew different or even contradictory conclusions. Performances of these algorithms over different terrain and surface cover conditions remain largely unknown. In this paper, we intercompared ten widely used topographic correction algorithms by adopting multi-criteria evaluation methods using Landsat images under various terrain and surface cover conditions as well as images simulated by a 3D radiative transfer model. Based on comprehensive analysis, we found that the Teillet regression-based models had the overall best performance in terms of topographic effects’ reduction and overcorrection; however, correction bias may be introduced by Teillet regression models when surface reflectance in the uncorrected images do not follow a normal distribution. We recommend including more simulated images for a more in-depth evaluation. We also recommend that the pros and cons of topographic correction methods reported in this paper should be carefully considered for surface parameters retrieval and applications in mountain regions

Experimentation and Simulation of the Combustion of Biomass Briquettes in Southern China

The thermogravimetry (TG) of typical biomass briquettes used as fuel in southern China was analysed to investigate the influences of fuel grain size and heating rate on combustion. The results suggested that grain size and heating rate exerted little influence on combustion. In accordance with the data and the TG results obtained from the fuel, a biomass grate incinerator process was numerically simulated using fluid dynamics-based incinerator code (FLIC) software to obtain the solid phase temperature distribution of the fuel along the bed length, the spatial temperature distribution of flue gas, and the underlying variation laws of the primary components. A comparison of the mass-loss curves from the numerical simulation to the TG analysis demonstrated that the two curves exhibited consistently staged variations, including dehydration and drying, fast pyrolysis and combustion of volatiles, and the burnout of residual carbon. The specific characteristics of the fuel obtained from these tests improved the accuracy of the numerical simulation, while the variations in temperature and components obtained were conducive to optimising the combustion process of a biomass incinerator

Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters

The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis in patients. However, accurate segmentation of unstained cell clusters containing more detailed features than the fluorescent staining images remains to be a challenging problem due to the complex background and the unclear boundary. Therefore, in this paper, we propose a fused 3-stage image segmentation algorithm, namely Coarse segmentation-Mapping-Fine segmentation (CMF) to achieve unstained cell clusters from whole slide images. Firstly, we establish a tumor cell cluster dataset consisting of 107 sets of images, with each set containing one unstained image, one stained image, and one ground-truth image. Then, according to the features of the unstained and stained cell clusters, we propose a three-stage segmentation method: 1) Coarse segmentation on stained images to extract suspicious cell regions-Region of Interest (ROI); 2) Mapping this ROI to the corresponding unstained image to get the ROI of the unstained image (UI-ROI); 3) Fine Segmentation using improved automatic fuzzy clustering framework (AFCF) on the UI-ROI to get precise cell cluster boundaries. Experimental results on 107 sets of images demonstrate that the proposed algorithm can achieve better performance on unstained cell clusters with an F1 score of 90.40%