Small sample block quality evaluation based on satellite images

Quantitative urban block quality evaluation is an important foundation for block design and planning, and image data is an important dimension of the block quality evaluation model. Currently, there are some problems in this field of research, such as the high cost of block quality labeling. This paper improves the small sample learning method based on subspace, performs singular decomposition on the satellite image features of the block to generate class sub-space, and inherits the subspace parameters of the training set into the block quality evaluation model. The experimental results show that this method is about 30% more accurate and 15% more consistent than the traditional small sample learning method.