Feature Extraction based on Extended Multi-Attribute Profiles and Sparse Autoencoder for Remote Sensing Image Classification

摘要

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become a challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper proposes a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral images "KSC" and "Pavia university" datasets and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers, and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.