Classification of Polarimetric SAR Images Using Multilayer Autoencoders and Superpixels

Abstract

A new polarimetric synthetic aperture radar (Pol-SAR) images classification method based on multilayer autoencoders and superpixels is proposed in this paper. First, in order to explore the spatial relations between pixels in PolSAR data, the RGB image formed with Pauli decomposition is used to produce superpixels to integrate contextual information of neighborhood. Second, multilayer autoencoders network is used to learning the features used for distinguishing the multiple categories for each pixel, and a softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. Finally, the probability distributions is regarded as a new probabilistic metric and introduced to k-nearest neighbor to improve the accuracy of classification based on superpixels, which takes spatial relationship between pixels into consideration, and it is robust to speckle noise. The proposed method makes good use of the scattering characteristics in each pixel and spatial information of PolSAR data. Compared with other state-of-the-art methods, the results of proposed method show better agreement with the ground truth and significant improvement in classification accuracy and discriminability of small differences between different categories.

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

“…Hou et al [60] propose SAE combined with superpixel for PolSAR image classification. The other stream of studies involves CNNs.…”

Contour Detection for UAV-Based Cadastral Mapping

“…As the cue combination is learned, based on a large number of natural images from the 'Berkeley Segmentation Dataset and Benchmark' [21], the approach seeks to be transferable to images of different contexts. Nevertheless, gPb contour detection has hardly been applied to remotely sensed data [27,28] and, to the best of the authors' knowledge, never to UAV data. The transferability of methods from computer vision to remote sensing is challenging, as both are often developed for image data with different characteristics: a benchmark dataset used in computer vision, such as the 'Berkeley Segmentation Dataset and Benchmark', contains natural images of maximal 1000 pixels in width and height, whereas a benchmark dataset used in remote sensing, such as the 'ISPRS Benchmark' [29], contains images from multiple sensors with higher numbers of pixels and larger ground sample distances (GSD).…”

Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach

“…For the multilayer autoencoders network, the numbers of neurons and layers are vital; they affect the quality of the network recovered by the training process and its ability to classify the test dataset [18]. To obtain better classification results, the numbers of neurons and layers are determined by the experiments.…”

“…For [18]. To obtain better classification results, the numbers of neurons and layers are determined by the experiments.…”

“…With this two-step optimization, multilayer autoencoders can not only prevent the network from overfitting when the number of labeled samples is relatively small but also extract effective features with its nonlinear mapping ability. Inspired by [18], we use a two-layer autoencoder to learn the features, and a softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When optimizing the network, SPL is used to accelerate its learning convergence and achieve a stronger generalization capability.…”