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.

翻译：高光谱图像因其分类复杂性而引起了研究人员的关注。高光谱图像中材料与光谱信息之间存在非线性关系。与传统机器学习方法相比，深度学习方法在学习这种非线性关系方面展现了优越性。使用三维CNN与二维CNN相结合的方法在学习空间和光谱特征方面取得了巨大成功。然而，这种方法需要相对较多的参数，且在层间信息学习方面效果不佳。因此，本文提出了一种结合三维CNN、二维CNN和双向长短期记忆网络的神经网络模型。该模型的性能在Indian Pines(IP)、University of Pavia(PU)和Salinas Scene(SA)数据集上进行了测试，并将结果与当前最先进的深度学习模型进行了比较。该模型在所有三个数据集上均表现更优。在IP、PU和SA数据集上，仅使用最先进模型30%的可训练参数，即可分别达到99.83%、99.98%和100%的准确率。