Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images

Scene classification can mine high-level semantic information scene categories from low-level visual features for high spatial resolution remote sensing images (HSRIs). A multifeature probabilistic latent semantic analysis (MPLSA) algorithm is proposed to perform the task of scene classification for HSRIs. Distinct from the traditional probabilistic latent semantic analysis (PLSA) with a single feature, to utilize the spatial information of the HSRIs, in MPLSA, multiple features, including spectral and texture features, and the scale-invariant feature transform feature, are combined with PLSA. The visual words are characterized by the multifeature descriptor, and an image set is represented by a discriminative word-image matrix. During the training phase, the MPLSA model mines the visual words’ latent semantics. For unknown images, the MPLSA model analyzes their corresponding latent semantic distributions by combining the words’ latent semantics obtained from the training step. The spectral angle mapper classifier is utilized to label the scene class, based on the image’s latent semantic distribution. The experimental results demonstrate that the proposed MPLSA method can achieve better scene classification accuracy than the traditional single-feature PLSA method.