USTER条干均匀度仪谱图分析及应用

本文重点介绍了应用USTER条干均匀度仪测定POY的波谱图、不匀曲线图以及CV值或U值,研究POY条干不匀的原因。根据USTER仪所记录的大量信息综合分析谱图特征及其与造成条干不匀的工艺性疵点和机械性疵点的关系。通过数据计算可以进一步确证机械性疵点产生的原因。这样,利用USTER仪进行监测,基本上就可以了解纺丝过程中工艺条件的设定及设备运转过程中存在的问题,达到及时改进工艺设计、改善设备运转状况不断提高POY质量的目的。

Contribution of Ground-Based Cloud Observation to Satellite-Based Cloud Discrimination

One of the biggest factors to deteriorate the satellite product quality is cloud coverage. Therefore, cloud masking process is important to improve the quality of various satellite products. However, satellite-based cloud discrimination algorithm has been developing and efficient ground-based cloud observations are necessary to validate satellite-based cloud discrimination. The purpose of this study is to develop the efficient ground-based cloud observation methodology using whole sky camera. This paper deals with methods how to discriminate cloud portions on whole sky image, how to apply the ground-based cloud observation to the validations for satellite products. For the cloud discrimination on whole sky image, we propose SI （sky index） and BI （brightness index） calculated from RGB （red, green and blue） channels. SI shows the extent of the blueness and gray scale and BI indicates the extent of the brightness. Sun, cloud and blue sky portions are divided by SI and BI threshold. As an application of ground-based cloud observation for the validation of satellite products, clouds portions discriminated from whole sky image are projected onto ground surface with map coordinate. We also examine to compare with cloud portions on whole sky images and MODIS （MODerate resolution Imaging Spectroradiometer） image as one of experiments. The proposed ground-based cloud observation method and its extension to satellite-based cloud discrimination should be connected to improve the quality of satellite products.

Spectral separability of tropical forest tree species using airborne hyperspectral imager

In Malaysia, airborne hyperspectral remote sensing is a relatively new technique used for research and commercial value in forest inventory and mapping. An advantage of airborne remote sensing, compared to satellite remote sensing, is its capability of offering a very high spatial resolution images. Thus, UPM-TropAIR AISA＇s airborne hyperspectral imagery that has been used in this study provides great quantity, better quality and also lower cost in identifying, quantifying and mapping of the Malaysian tropical timber forest resources. For the first stage in this study, the development of spectral library is deemed required in order for the Spectral Angle Mapper （SAM） classification be used to separate and map individual tree species in a tropical mixed mountain forest of Gunong Stong Forest Reserve. Pre-processing, enhancement and interpretation of image were conducted using ENVI Version 4.0 software. Results indicated that a total of eight commercial timber tree species was identified and mapped in a study plot of 5 ha using the TropAIR airborne hyperspectral imager with the aid of ground truthings.

Multi-scale MSDT inversion based on LAI spatial knowledge

Quantitative remote sensing inversion is ill-posed. The Moderate Resolution Imaging Spectroradiometer at 250 m resolution （MODIS_250m） contains two bands. To deal with this ill-posed inversion of MODIS_250m data, we propose a framework, the Multi-scale, Multi-stage, Sample-direction Dependent, Target-decisions （Multi-scale MSDT） inversion method, based on spa- tial knowledge. First, MODIS images （1 km, 500 m, 250 m） are used to extract multi-scale spatial knowledge. The inversion accuracy of MODIS_lkm data is improved by reducing the impact of spatial heterogeneity. Then, coarse-scale inversion is taken as prior knowledge for the fine scale, again by inversion. The prior knowledge is updated after each inversion step. At each scale, MODIS_lkm to MODIS_250m, the inversion is directed by the Uncertainty and Sensitivity Matrix （USM）, and the most uncertain parameters are inversed by the most sensitive data. All remote sensing data are involved in the inversion, during which multi-scale spatial knowledge is introduced, to reduce the impact of spatial heterogeneity. The USM analysis is used to implement a reasonable allocation of limited remote sensing data in the model space. In the entire multi-scale inversion process field data, spatial knowledge and multi-scale remote sensing data are all involved. As the multi-scale, multi-stage inversion is gradually refined, initial expectations of parameters become more reasonable and their uncertainty range is effectively reduced, so that the inversion becomes increasingly targeted. Finally, the method is tested by retrieving the Leaf Area Index （LAI） of the crop canopy in the Heihe River Basin. The results show that the proposed method is reliable.

Lunar potassium distribution:Results from Chang'E-1 gamma ray spectrometer

The global map of potassium is represented in this paper from Chang'E-1 (CE-1) Gamma-ray Spectrometer (CGRS) for its one-year mission.Assuming a linear relationship between net count rate and its abundance,the average potassium abundance of individual landing sites is used as ground-truth for the calibration to derive the global map of absolute concentration.Although CGRS spectra have a lower signal-to-noise ratio,the translated map still keeps relative variations.As calculated from Apollo,Lunar Prospector,and Kaguya,global potassium map from CGRS shows high concentrations on the lunar nearside and secondary concentrations located in the South Pole-Aitken (SPA) basin on the farside.The comparison with Lunar Prospector potassium map shows a good correlation,though abundances on the highlands of the farside are much lower than that of Lunar Prospector.Since the footprint of CGRS measurements is larger than the sampling radius of each landing site,the calibrated map shows a larger variation range of the scale than that of Lunar Prospector,which was derived using theoretical calculation;namely,the calibrated map has higher values in the areas with high concentration while having lower values for the areas with lower concentration.However,the derived potassium map is more consistent with the lunar sample data than that of Lunar Prospector.

A Fast Clonal Selection Algorithm for Feature Selection in Hyperspectral Imagery

Clonal selection feature selection algorithm （CSFS） based on clonal selection algorithm （CSA）, a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm （FCSFS） for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager （PHI） and the airborne visible/infrared imaging spectrometer （AVlRIS）, respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.