15 research outputs found
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 Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification
Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral-spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral-spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral-spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods