Journal Article

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

Zhou, Feng, Hang, Renlong, Yuan, Xiaotong, Liu, Qingshan

2017

Overview

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center). The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.

• Publisher: MDPI AG
• Subject: Artificial neural networks / bidirectional recurrent network / Classification / Colleges & universities / Convolution / convolution operator / Feature extraction / feature learning / hyperspectral image classification / Hyperspectral imaging / Image classification / Long short-term memory / Machine learning / Neural networks / Remote sensing / Representation learning / Short term / Spatial discrimination learning / Spatial memory / Spectra / Three dimensional models