ngyu Ji

Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks

Convolutional neural network (CNN) is well known for its capability of feature learning and has made revolutionary achievements in many applications, such as scene recognition and target detection. In this paper, its capability of feature learning in hyperspectral images is explored by constructing a five-layer CNN for classification (C-CNN). The proposed C-CNN is constructed by including recent advances in deep learning area, such as batch normalization, dropout, and parametric rectified linear unit (PReLU) activation function. In addition, both spatial context and spectral information are elegantly integrated into the C-CNN such that spatial-spectral features are learned for hyperspectral images. A companion feature-learning CNN (FL-CNN) is constructed by extracting fully connected feature layers in this C-CNN. Both supervised and unsupervised modes are designed for the proposed FL-CNN to learn sensor-specific spatial-spectral features. Extensive experimental results on four benchmark data sets from two well-known hyperspectral sensors, namely airborne visible/infrared imaging spectrometer (AVIRIS) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed C-CNN outperforms the state-of-the-art CNN-based classification methods, and its corresponding FL-CNN is very effective to extract sensor-specific spatial-spectral features for hyperspectral applications under both supervised and unsupervised modes.

Hyperspectral Image Classification by Exploring Low-Rank Property in Spectral or/and Spatial Domain

Within-class spectral variation, which is caused by varied imaging conditions, such as changes in illumination, environmental, atmospheric, and temporal conditions, significantly degrades the performance of hyperspectral image classification. Recent studies have shown that such spectral variation can be alleviated by exploring the low-rank property in the spectral domain, especially based on the low-rank subspace assumption. In this paper, the low-rank subspace assumption is approached by exploring the low-rank property in the local spectral domain. In addition, the low-rank property in the spatial domain is also explored to alleviate spectral variation. As a result, two novel spectral-spatial low-rank (SSLR) strategies are designed to alleviate spectral variation by exploring the low-rank property in both spectral and spatial domains. Experimental results on two benchmark hyperspectral datasets demonstrate that exploring the low-rank property in local spectral space can help to alleviate spectral variation and improve the performance of classification obviously for all tested data, while exploring the low-rank property in spatial space is more effective for images presenting large homogeneous areas.

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

Hyperspectral images are well-known for their fine spectral resolution to discriminate different materials. However, their spatial resolution is relatively low due to the trade-off in imaging sensor technologies, resulting in limitations in their applications. Inspired by recent achievements in convolutional neural network (CNN) based super-resolution (SR) for natural images, a novel three-dimensional full CNN (3D-FCNN) is constructed for spatial SR of hyperspectral images in this paper. Specifically, 3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated. Furthermore, a sensor-specific mode is designed for the proposed 3D-FCNN such that none of the samples from the target scene are required for training. Fine-tuning by a small number of training samples from the target scene can further improve the performance of such a sensor-specific method. Extensive experimental results on four benchmark datasets from two well-known hyperspectral sensors, namely hyperspectral digital imagery collection experiment (HYDICE) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed 3D-FCNN outperforms several existing SR methods by ensuring higher quality both in reconstruction and spectral fidelity.

Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification

Deep convolutional neural networks (CNNs) have brought in achievements in image classification and target detection. In this paper, we propose a novel five-layer CNN for hyperspectral classification by encountering recent achievement in deep learning area, such as batch normalization, dropout, Parametric Rectified Linear Unit (PReLu) activation function. By taking advantage of the specific characteristics of hyperspectral images, spatial context and spectral information are elegantly integrated into the framework. Experimental results demonstrate that our proposed CNN out- performs the state-of-the-art methods.

Spectral Variation Alleviation by Low-Rank Matrix Approximation for Hyperspectral Image Analysis

Spectral variation is profound in remotely sensed images due to variable imaging conditions. The wide presence of such spectral variation degrades the performance of hyperspectral analysis, such as classification and spectral unmixing. In this letter, 11-based low-rank matrix approximation is proposed to alleviate spectral variation for hyperspectral image analysis. Specifically, hyperspectral image data are decomposed into a low-rank matrix and a sparse matrix, and it is assumed that intrinsic spectral features are represented by the low-rank matrix and spectral variation is accommodated by the sparse matrix. As a result, the performance of image data analysis can be improved by working on the low-rank matrix. Experiments on benchmark hyperspectral data sets demonstrate the performance of classification, and spectral unmixing can be clearly improved by the proposed approach.

Learning sensor-specific features for hyperspectral images via 3-dimensional convolutional autoencoder

Deep learning techniques have brought in revolutionary achievements for feature learning of images. In this paper, a novel structure of 3-Dimensional Convolutional AutoEncoder (3D-CAE) is proposed for hyperspectral spatial-spectral feature learning, in which the spatial context is considered by constructing a 3-Dimensional input using pixels in a spatial neighborhood. All the parameters involved in the 3D-CAE are trained without the need of labeled training samples such that feature learning is conducted in an unsupervised fashion. Such unsupervised spatial-spectral feature extraction is also extended to different images from the same sensor to learn sensor-specific features. As a result, spatial-spectral features of hyperspectral images are extracted for a specific sensor under an unsupervised manner. Experimental results on several benchmark hyperspectral datasets have demonstrated that our proposed 3D-CAE are very effective in extracting sensor-specific spatial-spectral features and outperform several state-of-the-art deep learning neural networks in classification application.

Fusing different levels of deep features by deep stacked neural network for hyperspectral images

Deep learning techniques have been demonstrated to be a powerful tool to learn features of images automatically. In this paper, a novel deep learning structure, i.e., deep stacked neural network (DSNN), is constructed to extract different levels of deep features of hyperspectral images. Specifically, convolutional neural network (CNN) is used as basic units in the proposed DSNN for feature extraction of hyperspectral images. Then, different levels of deep features are concatenated to form a novel fused feature for classification with a typical classifier, e.g., SVM. Experimental results on two benchmark hyperspectral datasets show that the fusion of features extracted in DSNN can produce higher classification accuracy than state-of-the-art deep learning based methods, indicating its effectiveness in feature learning.

How to fully explore the low-rank property for data recovery of hyperspectral images

The performance of hyperspectral classification is affected by within-class spectral variation since different materials may present similar spectral signatures. In this paper, we investigate how to fully use the low-rank property of hyperspectral images to alleviate spectra variation. Particulary, two effective strategies that explore the low-rank property in local spectral and spatial space are proposed. According to experimental results, we conclude that exploring the low-rank property in local spectral-spatial space can help to alleviate spectral variation and improve the performance of classification obviously for all tested data, while exploring the low-rank property in spatial space is more effective for images presenting large homogeneous areas.