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.

使用 3D 卷积神经网络、2D 卷积神经网络和双向 LSTM，利用 30％可训练参数，在印度 Pines（IP）、帕维亚（PU）和萨利纳斯（SA）数据集上，这个模型在所有三个数据集上表现更好，分别达到 99.83％、99.98％和 100％的准确率，超越了最先进的深度学习模型。

混合 CNN Bi-LSTM 神经网络用于高光谱图像分类

Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification

Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural language description. In this context, we propose an
approach that successfully takes into account both the local and global
temporal structure of videos to produce descriptions. First, our approach
incorporates a spatial temporal 3-D convolutional neural network (3-D CNN)
representation of the short temporal dynamics. The 3-D CNN representation is
trained on video action recognition tasks, so as to produce a representation
that is tuned to human motion and behavior. Second we propose a temporal
attention mechanism that allows to go beyond local temporal modeling and learns
to automatically select the most relevant temporal segments given the
text-generating RNN. Our approach exceeds the current state-of-art for both
BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on
a new, larger and more challenging dataset of paired video and natural language
descriptions.

这篇论文提出了一种基于循环神经网络和 3-D 卷积神经网络以及时间注意力机制的视频自动描述方法，并在 Youtube2Text 数据集上的 BLEU 和 METEOR 度量标准上超过了当前最先进的结果，并在更大更具挑战性的配对视频和自然语言描述数据集上进行了测试。