In this paper we propose two improvements to standard JPEG (Joint Picture Expert Group) coding that can improve the compression ratio achieved for compressing remote sensing images obtained by sensors on-board micro-satellites by more than 39-60%. The first improvement consists in using a quantisation table that has been shown experimentally to be more appropriate for remote sensing images. The second and more significant improvement comes from the use of a novel region growing algorithm that can identify the outer border of a cloud region. The blocks that correspond to cloud regions are subsequently smoothed, as they represent unwanted information for the applications we are interested in, and encoded. The results are demonstrated with the help of several real images obtained by the Surrey University satellites.
The wide usage of small satellite imagery, especially its commercialization makes application based on-board compression not only meaningful but also necessary in order to solve the bottleneck between the huge volume of data generated on-board and the very limited downlink bandwidth. In this paper, we propose a method which encodes different regions with different algorithms. We use three shape-adaptive image compression algorithms to be the candidates. The first one is a JPEG-based algorithm; the second one is based on the Object- based Wavelet Transform (OWT) method proposed by Katata; the third adopts Hilbert scanning of the regions of interest followed by one dimensional (1-D) wavelet transform. The three algorithms are also applied to the full image so that we can compare their performance on who rectangular image. We use eight Landsat TM multi-spectral images as our test set. The results show that these compression algorithms have significantly different performance for different regions. For relatively smooth regions, e.g. regions that consist of a single type of vegetation or water areas etc., the 1-D wavelet method is the best; for highly textured regions, e.g. urban areas, mountain areas and so on, the modified OWT method wins over the others; for the whole image, OWT working at whole image mode, which is just an ordinary 2-D wavelet compression, is more suitable. Based on this, we propose a new application based compression architecture which encodes different regions with different algorithms.
On 31 May 2021, the Political Bureau of the Central Committee of the Communist Party of China proposed the policy that a couple can have three children, and rolled out more supportive measures to further optimize the fertility policies. However, while the Chinese government is further optimizing its fertility policy, the sudden outbreak of COVID-19 is raging around the world, which threatens the implementation of China's fertility optimization policy. Based on this, this paper firstly explores the impact of COVID-19 on women's fertility intentions. Secondly, based on the Theory of Planned Behavior, this paper constructs a structural equation model to quantitatively reveal the specific factors that affect women's fertility intentions under the epidemic, as well as their impact paths, and then puts forward corresponding suggestions for the government to solve the problem of fertility, aiming at delaying population aging and optimizing population structure. The research results show that: (1) COVID-19 lowers the fertility intentions of women of childbearing age. (2) During the pandemic, economic pressure emerged as the biggest factor affecting women's fertility intentions. The decline in income caused by the pandemic has become an important factor in preventing women from having children. (3) The conflict between work and childbearing is still an important factor affecting the fertility intentions of women of childbearing age. The government's provision of perfect childcare services and their strengthening of the protection of women's employment rights and interests will greatly reduce women's anxiety about childbearing.
Possibilistic c-means (PCM) based clustering algorithms are widely used in the literature. In this paper, we develop a noise level based PCM (NPCM) clustering algorithm. The advantage of NPCM is that strong prior information of the dataset is not required, and NPCM needs two kinds of information that is intuitive to specify for the clustering task, i.e., information of the cluster number and information of the property of clusters. More specifically, there are two parameters in NPCM: one specifies the possibly over-specified cluster number, and the other characterizes the closeness of clusters in the clustering result. Both parameters are not required to be exactly specified. Furthermore, we find that the update of bandwidth in adaptive PCM (APCM) is a positive feedback process and the adaptive bandwidth-uncertainty mechanism adopted in NPCM makes this positive feedback process more stronger, which leads to a faster convergence rate. Experiments show that the clustering process can be effectively controlled by the parameters.
The authors propose an improved version of JPEG coding for compressing remote sensing images obtained by optical sensors onboard microsatellites. The approach involves expanding cloud features to include their cloud-land transitions, thereby simplifying their coding and subsequent compression. The system is fully automatic and appropriate for onboard implementation. Its improvement in coding stems from the realization that a large number of bits are used for coding the blocks that contain the transition regions between bright clouds, if present in the image, and the dark background. A fully automatic cloud-segmentation algorithm is therefore used to identify the external boundaries of the clouds, then smooth the corresponding blocks prior to coding. Further gains are also achieved by modifying the quantization table used for coding the coefficients of the discrete cosine transform. Compared to standard JPEG, at the same level of reconstruction quality, the new method can achieve compression ratio improvement by 13-161%, depending upon the context and the amount of cloud present in the specific image. The results are demonstrated with the help of several real images obtained by the University of Surrey, U.K., satellites.
The wide usage of small satellite imagery, especially its commercialization, makes data-based onboard compression not only meaningful but also necessary in order to solve the bottleneck between the huge volume of data generated onboard and the very limited downlink bandwidth. The authors propose a method that encodes different regions with different algorithms. The authors use three shape-adaptive image compression algorithms as the candidates. The first one is a JPEG-based algorithm, the second one is based on the object-based wavelet transform method proposed by Katata et al. (1997), and the third adopts Hilbert scanning of the regions of interest followed by one-dimensional (1-D) wavelet transform. The three algorithms are also applied to the full image so that one can compare their performance on a whole rectangular image. The authors use eight Landsat TM multispectral images and another 12 small satellite single-band images as their data set. The results show that these compression algorithms have significantly different performance for different regions.
In response to the population aging, on May 31, 2021, the Political Bureau of the Central Committee of the Communist Party of China (CPC) proposed the policy that a couple can have three children and rolled out more supportive measures to further optimize fertility policies, which is another major initiative following the universal two-child policy introduced in November 2015. Currently, a series of population policy innovations have aroused great attractions among the public and triggered a hot debating on the Internet. People's fertility attitude tendency under different related policies can reflect their current fertility intentions. Based on the fact, this paper firstly classifies the sentiment of online comment data on the three-child policy and analyzes people's sentiment tendency toward the three-child policy from the spatio-temporal perspectives. Secondly, people's points of view on the three-child policy are summarized by using Latent Dirichlet Allocation (LDA) thematic clustering. The reasons for the change in people's fertility attitude tendency under different fertility policies are analyzed by comparing the change in people's fertility attitude tendency with the change in people's attentions. Finally, a multiple regression equation is constructed to analyze the key factors influencing people's intention to have three children by using public opinion data and its extension data. The findings demonstrate: (1) people's fertility attitudes toward the three-child policy are negative and similar among different regions; (2) compared to the two-child policy, the percentage of negative and neutral attitudes toward the three-child policy increases, while the percentage of positive attitudes decreases; (3) the increase in fertility costs, the deterioration of women's employment environment, and the change in the concept of marriage and childbirth become important reasons for the negative change in people's fertility attitudes toward different policies. Therefore, the government should take measures to reduce the burden of childbirth and guide the correct concept of marriage and childbirth to improve people's fertility intentions.
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