Realization Technology of Texture Feature Extraction Algorithm of Remote Sensing Satellite Image Based on FPGA

Applications of Multiscale Geometric Analysis in Image Texture Recognition and Classification

“…With three decomposition levels and a direction number configuration of (8,8,8), the feature dimension was 51, feature extraction time was 62 milliseconds, classification time without dimension reduction was 152 milliseconds, and the recognition rate was 92.56%, which significantly increased to 96.82% after using KSR dimension reduction. In four decomposition levels, using a direction number configuration of (4,8,8,16), the feature extraction time reached 81 milliseconds, classification time without dimension reduction was 178 milliseconds, and the recognition rate was 93.68%, stabilizing at 96.87% after KSR reduction. These data suggest that by increasing decomposition levels and adjusting direction numbers, classification accuracy can be significantly improved at the expense of some computational efficiency.…”

“…However, existing methods of texture analysis have some shortcomings. Firstly, many traditional texture feature extraction methods are sensitive to noise and lighting changes, easily affected by environmental factors, which impacts the accuracy of classification [14][15][16][17]. Secondly, these methods often perform poorly when dealing with highly non-uniform or multiscale textures, failing to adequately capture the details and layers within complex textures [18][19][20].…”