269 research outputs found
Hyperspectral Image Analysis through Unsupervised Deep Learning
Hyperspectral image (HSI) analysis has become an active research area in computer vision field with a wide range of applications. However, in order to yield better recognition and analysis results, we need to address two challenging issues of HSI, i.e., the existence of mixed pixels and its significantly low spatial resolution (LR). In this dissertation, spectral unmixing (SU) and hyperspectral image super-resolution (HSI-SR) approaches are developed to address these two issues with advanced deep learning models in an unsupervised fashion. A specific application, anomaly detection, is also studied, to show the importance of SU.Although deep learning has achieved the state-of-the-art performance on supervised problems, its practice on unsupervised problems has not been fully developed. To address the problem of SU, an untied denoising autoencoder is proposed to decompose the HSI into endmembers and abundances with non-negative and abundance sum-to-one constraints. The denoising capacity is incorporated into the network with a sparsity constraint to boost the performance of endmember extraction and abundance estimation.Moreover, the first attempt is made to solve the problem of HSI-SR using an unsupervised encoder-decoder architecture by fusing the LR HSI with the high-resolution multispectral image (MSI). The architecture is composed of two encoder-decoder networks, coupled through a shared decoder, to preserve the rich spectral information from the HSI network. It encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. And the angular difference between representations are minimized to reduce the spectral distortion.Finally, a novel detection algorithm is proposed through spectral unmixing and dictionary based low-rank decomposition, where the dictionary is constructed with mean-shift clustering and the coefficients of the dictionary is encouraged to be low-rank. Experimental evaluations show significant improvement on the performance of anomaly detection conducted on the abundances (through SU).The effectiveness of the proposed approaches has been evaluated thoroughly by extensive experiments, to achieve the state-of-the-art results
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection
Current hyperspectral anomaly detection (HAD) benchmark datasets suffer from
low resolution, simple background, and small size of the detection data. These
factors also limit the performance of the well-known low-rank representation
(LRR) models in terms of robustness on the separation of background and target
features and the reliance on manual parameter selection. To this end, we build
a new set of HAD benchmark datasets for improving the robustness of the HAD
algorithm in complex scenarios, AIR-HAD for short. Accordingly, we propose a
generalized and interpretable HAD network by deeply unfolding a
dictionary-learnable LLR model, named LRR-Net, which is capable of
spectrally decoupling the background structure and object properties in a more
generalized fashion and eliminating the bias introduced by vital interference
targets concurrently. In addition, LRR-Net integrates the solution process
of the Alternating Direction Method of Multipliers (ADMM) optimizer with the
deep network, guiding its search process and imparting a level of
interpretability to parameter optimization. Additionally, the integration of
physical models with DL techniques eliminates the need for manual parameter
tuning. The manually tuned parameters are seamlessly transformed into trainable
parameters for deep neural networks, facilitating a more efficient and
automated optimization process. Extensive experiments conducted on the AIR-HAD
dataset show the superiority of our LRR-Net in terms of detection
performance and generalization ability, compared to top-performing rivals.
Furthermore, the compilable codes and our AIR-HAD benchmark datasets in this
paper will be made available freely and openly at
\url{https://sites.google.com/view/danfeng-hong}
Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection
In recent years, multi-view subspace learning has been garnering increasing
attention. It aims to capture the inner relationships of the data that are
collected from multiple sources by learning a unified representation. In this
way, comprehensive information from multiple views is shared and preserved for
the generalization processes. As a special branch of temporal series
hyperspectral image (HSI) processing, the anomalous change detection task
focuses on detecting very small changes among different temporal images.
However, when the volume of datasets is very large or the classes are
relatively comprehensive, existing methods may fail to find those changes
between the scenes, and end up with terrible detection results. In this paper,
inspired by the sketched representation and multi-view subspace learning, a
sketched multi-view subspace learning (SMSL) model is proposed for HSI
anomalous change detection. The proposed model preserves major information from
the image pairs and improves computational complexity by using a sketched
representation matrix. Furthermore, the differences between scenes are
extracted by utilizing the specific regularizer of the self-representation
matrices. To evaluate the detection effectiveness of the proposed SMSL model,
experiments are conducted on a benchmark hyperspectral remote sensing dataset
and a natural hyperspectral dataset, and compared with other state-of-the art
approaches
A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique
- …