Stacked Autoencoder Based Feature Extraction and Superpixel Generation for Multifrequency PolSAR Image Classification
This work addresses classification challenges in remote sensing for PolSAR images, but it is incremental as it combines existing techniques like autoencoders and SLIC in a new pipeline.
The paper tackles classification of multifrequency PolSAR images by extracting features, reducing dimensionality with a stacked autoencoder, generating superpixels using SLIC to preserve spatial information and reduce speckle noise, and classifying with a softmax classifier. It reports superior performance on the Flevoland dataset compared to existing methods.
In this paper we are proposing classification algorithm for multifrequency Polarimetric Synthetic Aperture Radar (PolSAR) image. Using PolSAR decomposition algorithms 33 features are extracted from each frequency band of the given image. Then, a two-layer autoencoder is used to reduce the dimensionality of input feature vector while retaining useful features of the input. This reduced dimensional feature vector is then applied to generate superpixels using simple linear iterative clustering (SLIC) algorithm. Next, a robust feature representation is constructed using both pixel as well as superpixel information. Finally, softmax classifier is used to perform classification task. The advantage of using superpixels is that it preserves spatial information between neighbouring PolSAR pixels and therefore minimises the effect of speckle noise during classification. Experiments have been conducted on Flevoland dataset and the proposed method was found to be superior to other methods available in the literature.