Urban remote sensing image scene classification method in consideration of spatial relationships

An urban remote sensing image scene classification method in consideration of spatial relationships, comprising:

• step 1, cutting a remote sensing image into sub-images in an even and non-overlapping manner;
• step 2, performing a visual information coding on each of the sub-images to obtain a feature image Fv;
• step 3, inputting the feature image Fv into a crossing transfer unit to obtain hierarchical spatial characteristics;
• step 4, performing convolution of dimensionality reduction on the hierarchical spatial characteristics to obtain dimensionality-reduced hierarchical spatial characteristics;
• and
• step 5, performing a softmax model based classification on the dimensionality-reduced hierarchical spatial characteristics to obtain a classification result;
• wherein the step 1 of cutting a remote sensing image into sub-images in an even and non-overlapping manner in the step 1 comprises that:
• a large-scale remote sensing image I with a size of M×N is sliding cut into m×n sub-images in the even and non-overlapping manner, each of the sub-images Pi,j is with a size of
• M
• m
• ×
• N
• n
• ,
• row and column numbers (i, j) of the Pi,j in the I are stored as spatial information, where M, N, m and n are positive integers, 1≤i≤m, and 1≤j≤n;
• wherein the step 2 of performing a visual information coding on each of the sub-images to obtain a feature image Fv comprises that:
• a pre-trained deep convolution model is used to perform the visual information coding on each of the sub-images Pi,j to convert the Pi,j into a vector fvi,j, and thereby the large-scale remote sensing image I is converted into the feature image Fv:
• Fv
• =
• (
• fv
• 1
• ,
• 1
• …
• fv
• 1
• ,
• n
• ⋮
• ⋱
• ⋮
• fv
• m
• ,
• 1
• …
• fv
• m
• ,
• n
• )
• ;
• wherein the crossing transfer unit is used for extraction and fusion of regional spatial relationship and long-range spatial relationship, an extraction formula of the regional spatial relationship is Fr=Conv(Fv)=Fv*W+b, where Fr represents spatial relationship as extracted for analysis, Conv( ) represents a convolution function, W represents a convolution kernel, B represents an offset, and * represents a convolution operation;
• and the long-range spatial relationship is extracted by a ReNet module based on a recurrent neural network.