194 research outputs found
Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification
Hyper spectral images have drawn the attention of the researchers for its
complexity to classify. It has nonlinear relation between the materials and the
spectral information provided by the HSI image. Deep learning methods have
shown superiority in learning this nonlinearity in comparison to traditional
machine learning methods. Use of 3-D CNN along with 2-D CNN have shown great
success for learning spatial and spectral features. However, it uses
comparatively large number of parameters. Moreover, it is not effective to
learn inter layer information. Hence, this paper proposes a neural network
combining 3-D CNN, 2-D CNN and Bi-LSTM. The performance of this model has been
tested on Indian Pines(IP) University of Pavia(PU) and Salinas Scene(SA) data
sets. The results are compared with the state of-the-art deep learning-based
models. This model performed better in all three datasets. It could achieve
99.83, 99.98 and 100 percent accuracy using only 30 percent trainable
parameters of the state-of-art model in IP, PU and SA datasets respectively
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the
analysis of hyperspectral image sequences. It reveals the dynamical evolution
of the materials (endmembers) and of their proportions (abundances) in a given
scene. However, adequately accounting for the spatial and temporal variability
of the endmembers in MTHU is challenging, and has not been fully addressed so
far in unsupervised frameworks. In this work, we propose an unsupervised MTHU
algorithm based on variational recurrent neural networks. First, a stochastic
model is proposed to represent both the dynamical evolution of the endmembers
and their abundances, as well as the mixing process. Moreover, a new model
based on a low-dimensional parametrization is used to represent spatial and
temporal endmember variability, significantly reducing the amount of variables
to be estimated. We propose to formulate MTHU as a Bayesian inference problem.
However, the solution to this problem does not have an analytical solution due
to the nonlinearity and non-Gaussianity of the model. Thus, we propose a
solution based on deep variational inference, in which the posterior
distribution of the estimated abundances and endmembers is represented by using
a combination of recurrent neural networks and a physically motivated model.
The parameters of the model are learned using stochastic backpropagation.
Experimental results show that the proposed method outperforms state of the art
MTHU algorithms
DC-SAM: DILATED CONVOLUTION AND SPECTRAL ATTENTION MODULE FOR WHEAT SALT STRESS CLASSIFICATION AND INTERPRETATION
Salt stress can impact wheat production significantly and is difficult to be managed when the condition is critical. Hence, detecting such stress whet it is at an early stage is important. This paper proposed a deep learning method called Dilated Convolution and Spectral Attention Module (DC-SAM), which exploits the difference in spectral responses of healthy and stressed wheat. The proposed DC-SAM method consists of two key modules:Â (i)Â a dilated convolution module to capture spectral features with large receptive field;Â (ii)Â a spectral attention module to adaptively fuse the spectral features based on their interrelationship. As the dilated convolution module has long receptive fields, it can capture short- and long dependency patterns that exist in hyperspectral data. Our experimental results with four datasets show that DC-SAM outperforms existing state-of-the-art methods. Also, the output of the proposed attention module reveals the most discriminative spectral bands for a given wheat stress classification task
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