The crowd degree of buildings is a very important aspect for the assessment of environmental quality of urban communities. Remote sensing imagery with high spatial resolution provides more detailed spatial information about land covers and makes it possible to assess the quality of communities in detailed scale. Four factors were proposed to assess the crowd degree of buildings based on the combination of high spatial resolution imagery and some fundamental principles including estate and geostatistics methods. Five typical communities in Xiamen City were selected to demonstrate the application of those indices. Results suggested that these indices could describe the building crowd degree from different aspects, and it provided a new approach in assessing environmental quality of community through high spatial resolution remote sensed data.

Recently, the environmental mapping is an important application of high spatial resolution remote sensing images. However, with the improvement of spatial resolution, the size and land cover details of imagery significantly increase. Those bring on some hurdles to image processing. In this paper, some key processing techniques are discussed including the choice of spatial and pixel resolution, color match and recovering. Actual works proves that these methods are very useful for environmental mapping based on high spatial resolution remote sensed images.

A new class of approach for image restoration based on two-dimensional Cellular Automata (CA) is proposed. By researching two new kinds of two-dimensional CA, we find two new classes of CA rules to design CNN, which can implement image restoration. The main idea is to design two CNNs with two antithetic CA rules. By this way, Image restoration can be implemented, getting obviously better results than the traditional ones. The simulation results prove our idea reasonable. It is expected to have profound influence on the technology of two-dimensional CA.