3,264 research outputs found
High-quality hyperspectral reconstruction using a spectral prior
We present a novel hyperspectral image reconstruction algorithm, which overcomes the long-standing tradeoff between spectral accuracy and spatial resolution in existing compressive imaging approaches. Our method consists of two steps: First, we learn nonlinear spectral representations from real-world hyperspectral datasets; for this, we build a convolutional autoencoder, which allows reconstructing its own input through its encoder and decoder networks. Second, we introduce a novel optimization method, which jointly regularizes the fidelity of the learned nonlinear spectral representations and the sparsity of gradients in the spatial domain, by means of our new fidelity prior. Our technique can be applied to any existing compressive imaging architecture, and has been thoroughly tested both in simulation, and by building a prototype hyperspectral imaging system. It outperforms the state-of-the-art methods from each architecture, both in terms of spectral accuracy and spatial resolution, while its computational complexity is reduced by two orders of magnitude with respect to sparse coding techniques. Moreover, we present two additional applications of our method: hyperspectral interpolation and demosaicing. Last, we have created a new high-resolution hyperspectral dataset containing sharper images of more spectral variety than existing ones, available through our project website
Effect of top-down connections in Hierarchical Sparse Coding
Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent
multi-dimensional, structured data such as images. The simplest solution to
solve this computationally hard problem is to decompose it into independent
layer-wise subproblems. However, neuroscientific evidence would suggest
inter-connecting these subproblems as in the Predictive Coding (PC) theory,
which adds top-down connections between consecutive layers. In this study, a
new model called 2-Layers Sparse Predictive Coding (2L-SPC) is introduced to
assess the impact of this inter-layer feedback connection. In particular, the
2L-SPC is compared with a Hierarchical Lasso (Hi-La) network made out of a
sequence of independent Lasso layers. The 2L-SPC and the 2-layers Hi-La
networks are trained on 4 different databases and with different sparsity
parameters on each layer. First, we show that the overall prediction error
generated by 2L-SPC is lower thanks to the feedback mechanism as it transfers
prediction error between layers. Second, we demonstrate that the inference
stage of the 2L-SPC is faster to converge than for the Hi-La model. Third, we
show that the 2L-SPC also accelerates the learning process. Finally, the
qualitative analysis of both models dictionaries, supported by their activation
probability, show that the 2L-SPC features are more generic and informative
Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters
Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable
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