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High altitude balloon is a kind of aerostat working in the near space. Based on the basic radiation theory of physical optics, this paper takes the high altitude balloon as the research object. On the basis of summarizing and analyzing the research status of the high altitude balloon. Through the study of the thermodynamic model, the infrared radiation characteristics of the high altitude balloon are simulated. Through the study of the characteristics of the environmental factors of the high altitude balloon in the adjacent space, the infrared radiation of the environment of the high altitude balloon is simulated. Through the study of common atmospheric models, the atmospheric transmittance in the process of infrared radiation transmission is simulated. Finally, based on the linear quantization method, the radiation value is transformed into gray value, and then the infrared simulation image of high altitude balloon is obtained. The research and simulation results of this paper have certain reference value for the infrared detection and recognition of the high altitude balloon target.

Image segmentation has always been a key research issue in the field of computer vision. Image segmentation networks that use deep learning methods require a large number of finely labeled samples, which is difficult to obtain. In this paper, we combine the focal loss function with the fully convolutional networks to improve network performance. And we collected and built a dataset contents 1500 samples with complex background. We trained the improved network with the dataset to achieve 81.55% in mean average precision and 76.13% in mean intersection over union.

We have witnessed the accumulation of petabyes of geospatial data in the past decades, and, currently, terabytes of data are collected every day. These geospatial data are crucial in supporting decision making and emergency response. It becomes increasingly important to deliver these datasets in a timely fashion to the decision support or emergency response systems. Network GIS provides a vehicle to facilitate this delivering process. But to deliver efficiently such large volume of data and to handle large number of concurrent users, the performance of Network GIS needs to be improved to a level that different types of applications, especially near real time applications, can be satisfied. In this circumstance, we 1) review selected research on improving the performance of Network GIs; 2) provide insides on implementing the techniques; and 3) illustrate how to adopt the techniques in Network GIs. We expect the research and development reported here can be easily adopted by different users to accelerate the performance of various Network GIS software and applications, as well as to support the building of spatial data infrastructure to support the sharing of heterogeneous geospatial information.

Global pollution aerosol monitoring is a very important climatic and environmental problem. It affects not only human health but also ecological systems. Because most pollution aerosols are concentrated in the atmospheric boundary layer where human, animal and vegetation live, global pollution aerosol stuides have been an important topic since about a decade ago. Recently, many new chemistry remote sensing satellite systems, such as NASA's Aura (EOS-CHEM), have been established. However, pollution aerosols in the atmospheric boundary layer cannot be detected using current remote sensing technologies. George Mason University (GMU) proposes to design scientific algorithms and technologies to monitor the atmospheric boundary layer pollution aerosols, using both satellite remote sensing measurements and ground measurements, collaborating with NASA and the United States Department of Agriculture (USDA)/Forest Services (FS). Boundary layer pollution aerosols result from industrial pollution, desert dust storms, smoke from wildfires and biomass burning, volcanic eruptions, and from other trace gases. The current and next generation satellite instruments, such as The Ozone Mapping and Profiler Suite (OMPS), Ozone Monitoring Instrument (OMI), Thermal Emission Spectrometer (TES), and High Resolution Dynamics Limb Sounder (HIRDLS) can be used for this study. Some surface measurements from USDA/FS and other agencies may also be used in this study. We will discuss critical issues for GPAM in the boundary layer using Earth observing satellite remote sensing in detail in this paper.

Virginia Access (VAccess) is a regional, remote sensing and Geographical Information Sciences project among several educational institutions. It is a prototype for regional projects in other states and other countries, and is funded by NASA's applications program. The user communities VAccess serves are the Commonwealth of Virginia and State of Maryland, local and regional users represented in a Technical Advisory Committee. Remote sensing data include global NASA and NOAA data tailored for regional applications as well as high-resolution multispectral (Landsat, MODIS, etc.), hyperspectral, LIDAR and SAR data sets. Broad beam LIDAR technology can provide canopy structure as well as other information for environmental concerns such as the state of wetlands. The data information system is based on a distributed architecture to serve remote sensing and GIS data to a variety of users via the WWW. Several remote sensing and GIS-based environmental and Earth systems science applications projects are discussed here, including flood and fire hazard mitigation, forestry, land use/land cover and epidemiology projects; as well as innovative data fusion, data access and analysis and various tools serving the users and their applications.

The Normalized Difference Vegetation Index (NDVI), provide synoptic information on environmental conditions in agricultural regions such as in Ukraine and Russia. However, past studies have raised questions whether the Maximum Value Compositing (MVC) method, used to produce time-series of NDVI, is affected by Bidirectional Reflectance Distributions (BRD), as a result of view geometry, possibly leading to erroneous NDVI values. This study uses Angular Dependence Models (ADM), derived the NOAA/NASA Pathfinder AVHRR Land (PAL) data set, to correct PAL Channels 1 & 2 reflectance for BR. MVC is used to produce ten day NDVI composites, from both the pre-BRD correct channels and the BRD corrected channels. Comparisons of the two NDVI time-series show a slight increase in NDVI for the

Some types of clay, esp. montmorillonite, become slippery when getting wet. Clay movement is very harmful for various constructions and can also cause trouble for both wheeled and tracked vehicles in military operations at some rural areas when raining. We present a summary of a project using hyperspectral imaging in assisting earth roads construction planning and cross-country trafficability analysis. Spectral signature libraries are used to help identify materials and define those areas to be avoided, which have significant montmorillonite content. We perform a case study in this kind of application; some methods of data processing and analyzing are discussed. We also discussed the problems we met in this application. Hyperspectral sensing is a relatively new but mature technology; development of applications and corresponding analyzing procedures will be the major impetus of this technology.