WiMi Hologram Cloud develops RPSSC technology

WiMi Hologram Cloud developed RandomPatchSpatialSpectrumClassifier technology to fully utilize the complementarity between spatial and spectral information. The R&D of WiMi‘s RPSSC combines a 2D Gabor filter and a random patch convolution feature extraction method. First, the RPSSC uses principal component analysis and LDA algorithms to downscale the original hyperspectral image. The purpose of this step is to eliminate redundant spectral information while retaining the main information, increase the inter- and intra-class distance ratios, and prepare data for subsequent feature extraction and classification. On the dimensionalized image, RPSSC introduces a two-dimensional Gabor filter. Gabor filters are widely used in the field of computer vision to extract spatial structural features such as edges and textures of images. Through the Gabor filter, the RPSSC technology can capture the local texture and spatial information in the image, which lays the foundation for the subsequent feature extraction. Next, employed the GRPC method to the RPSSC, which takes Gabor features as input. Random patch convolution realizes the extraction of multilevel spectral features from an image by randomly selecting patches in the image and performing convolution operations on these patches. This step aims to synthesize spatial and spectral information, allowing the model to understand the features of the image more comprehensively. Finally, the RPSSC technology fuses the spatial features extracted from the GRPC with the multilevel spectral features. Through this fusion process, the model is able to synthesize the spectral information and local spatial structure information to provide a richer feature representation for image classification. Ultimately, RPSSC employs a support vector machine (SVM) classifier to classify the fused features to achieve accurate classification of hyperspectral images.