DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification

Deep neural networks face many problems in the field of hyperspectral image classification, lack of effective utilization of spatial spectral information, gradient disappearance and overfitting as the model depth increases. In order to accelerate the deployment of the model on edge devices with strict latency requirements and limited computing power, we introduce a lightweight model based on the improved 3D-Densenet model and designs DGCNet. It improves the disadvantage of group convolution. Referring to the idea of dynamic network, dynamic group convolution(DGC) is designed on 3d convolution kernel. DGC introduces small feature selectors for each grouping to dynamically decide which part of the input channel to connect based on the activations of all input channels. Multiple groups can capture different and complementary visual and semantic features of input images, allowing convolution neural network(CNN) to learn rich features. 3D convolution extracts high-dimensional and redundant hyperspectral data, and there is also a lot of redundant information between convolution kernels. DGC module allows 3D-Densenet to select channel information with richer semantic features and discard inactive regions. The 3D-CNN passing through the DGC module can be regarded as a pruned network. DGC not only allows 3D-CNN to complete sufficient feature extraction, but also takes into account the requirements of speed and calculation amount. The inference speed and accuracy have been improved, with outstanding performance on the IN, Pavia and KSC datasets, ahead of the mainstream hyperspectral image classification methods

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends