KEYWORDS: Data fusion, Data modeling, Environmental sensing, Machine learning, Mining, Target recognition, Data acquisition, Atmospheric modeling, Deep learning, Information fusion
With the rapid development and widespread application of information technology, various information resources targeting complex application scenarios have been continuously enriched. Consequently, a large number of information management systems have emerged, and the capacity and processing requirements of information have far exceeded the capabilities of traditional processing methods. This presents significant challenges for comprehensive environmental perception that encompasses multiple sources of data from geographic, surveying, meteorological, oceanic, and human-related domains, involving the entire process from data generation to knowledge acquisition. In this paper, firstly, the concept and connotation of environmental comprehensive perception are expounded. Secondly, the key technology system of environmental comprehensive perception is explored, and the key links and cutting-edge research status of multi-source data acquisition, processing, management, analysis, mining and service of environmental comprehensive perception are sorted out, and a hierarchical environmental comprehensive perception system is constructed. Finally, the development trend of environmental comprehensive perception is prospected.
Line features are very important in photogrammetry, and compression often causes a loss of line features in images. In
order to study the effects of compression on feature extraction, a SPOT5 image was selected as test data and the image
was compressed with seven compression ratios. Features of the original and compressed images were extracted with the
Canny operator. A change detection method was used to compare the features of the original and compressed images.
The results show that the features extracted vary with the compression ratio, and the change ratio for those features
increases with increasing compression ratio.
Satellite images and aerial images have different radiation attributes. Conclusions on aerial image compression effects
may not be applicable to satellite images. In this paper we study the effects of lossy compression on the digital terrain
model (DTM) derived from a selected stereo pair of SPOT-5 satellite images. The satellite images are compressed by the
Kakadu JPEG2000 compression software at the compression ratios of 2, 4, 6, and 8. The Imageinfo Pixelgrid V2.0
software is used as a DTM generator. The DTM results derived from original images are compared with that derived
from compressed images in terms of the mean error and the standard deviation. This paper's experiment indicates that
when the compression ratio rises to 4, both the DTM generation and the stereo observation by human will be affected
dramatically.
This paper first introduces the character of JPEG2000 and JPEG, a digital stereo aerial image pair is compressed using the JPEG2000 and JPEG method. Comparing are provided from subjective quality, PSNR and accuracy of digital terrain models (DTM) automatically derived from digital stereo aerial image pair. Experiment analysis is provided in the end, and result indicates that the JPEG2000 method has better effect.
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