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。