Due to the unique feature of the three-dimensional convolution neural network, it is used in image classification. There are some problems such as noise, lack of labeled samples, the tendency to overfitting, a lack of extraction of spectral and spatial features, which has challenged the classification. Among the mentioned problems, the lack of experimental samples is the main problem that has been used to solve the methods in recent years. Among them, convolutional neural network-based algorithms have been proposed as a popular option for hyperspectral image analysis due to their ability to extract useful features and high performance. The traditional convolutional neural network (CNN) based methods mainly use the two-dimensional CNN for feature extraction, which makes the interband correlations of HSIs underutilized. The 3-D-CNN extracts the joint spectral–spatial information representation, but it depends on a more complex model. To address these issues, the report uses a 3-D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2-D convolutional neural network was introduced to extract spectral-spatial features. Using a hybrid CNN reduces the complexity of the model compared to using 3-D-CNN alone and can also perform well against noise and a limited number of training samples. In addition, a series of optimization methods including batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To test the performance of this hybrid method, it is performed on the Salinas, University Pavia and Indian Pines datasets, and the results are compared with 2-D-CNN and 3-D-CNN deep learning models with the same number of layers.

/pdf/hyperspectral-image-classification-using-a-hybrid-3d-2d-lxmo57gwc5.pdf

Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks

Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band (f...

Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification

Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast patch-free global learning (FPGA) architecture was proposed for HSI classification according to global spatial context information. However, FPGA has difficulty in extracting the most discriminative features when the sample data are imbalanced. In this article, a spectral-spatial-dependent global learning (SSDGL) framework based on the global convolutional long short-term memory (GCL) and global joint attention mechanism (GJAM) is proposed for insufficient and imbalanced HSI classification. In SSDGL, the hierarchically balanced (H-B) sampling strategy and the weighted softmax loss are proposed to address the imbalanced sample problem. To effectively distinguish similar spectral characteristics of land cover types, the GCL module is introduced to extract the long short-term dependency of spectral features. To learn the most discriminative feature representations, the GJAM module is proposed to extract attention areas. The experimental results obtained with three public HSI datasets show that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods.

A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification.

Convolutional neural networks (CNNs) have become quite popular for solving many different tasks in remote sensing data processing. The convolution is a linear operation, which extracts features from the input data. However, nonlinear operations are able to better characterize the internal relationships and hidden patterns within complex remote sensing data, such as hyperspectral images (HSIs). Morphological operations are powerful nonlinear transformations for feature extraction that preserve the essential characteristics of the image, such as borders, shape, and structural information. In this article, a new end-to-end morphological deep learning framework (called MorphConvHyperNet ) is introduced. The proposed approach efficiently models nonlinear information during the training process of HSI classification. Specifically, our method includes spectral and spatial morphological blocks to extract relevant features from the HSI input data. These morphological blocks consist of two basic 2-D morphological operators (erosion and dilation) in the respective layers, followed by a weighted combination of the feature maps. Both layers can successfully encode the nonlinear information related to shape and size, playing an important role in classification performance. Our experimental results, obtained on five widely used HSIs, reveal that our newly proposed MorphConvHyperNet offers comparable (and even superior) performance when compared to traditional 2-D and 3-D CNNs for HSI classification.

/pdf/morphological-convolutional-neural-networks-for-441mevu38f.pdf

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

https://www.mdpi.com/1996-1073/13/23/6241/pdf

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

Hyperspectral image (HSI) classification is a most challenging task in hyperspectral remote sensing field due to unique characteristics of HSI data. It consists of huge number of bands with strong ...

Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification

The deep convolutional neural network (CNN) has recently attracted the researchers for classification of hyperspectral remote sensing images. The CNN mainly consists of convolution layer, pooling layer and fully connected layer. The pooling is a regularisation technique and improves the performance of CNN while reducing the computation time. Various pooling strategies have been developed in literature. This study shows the effect of pooling strategy on the performance of deep CNN for classification of hyperspectral remote sensing images. The authors have compared the performance of various pooling strategies such as max pooling, average pooling, stochastic pooling, rank-based average pooling and rank-based weighted pooling. The experiments were performed on three well-known hyperspectral remote sensing datasets: Indian Pines, University of Pavia and Kennedy Space Center. The proposed experimental results show that max pooling has produced better results for all the three considered datasets.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.0561

Effect of pooling strategy on convolutional neural network for classification of hyperspectral remote sensing images

Hyperspectral image (HSI) classification has been one of the most important tasks in the remote sensing community over the last few decades. Due to the presence of highly correlated bands and limited training samples in HSI, discriminative feature extraction was challenging for traditional machine learning methods. Recently, deep learning based methods have been recognized as powerful feature extraction tool and have drawn a significant amount of attention in HSI classification. Among various deep learning models, convolutional neural networks (CNNs) have shown huge success and offered great potential to yield high performance in HSI classification. Motivated by this successful performance, this paper presents a systematic review of different CNN architectures for HSI classification and provides some future guidelines. To accomplish this, our study has taken a few important steps. First, we have focused on different CNN architectures, which are able to extract spectral, spatial, and joint spectral-spatial features. Then, many publications related to CNN based HSI classifications have been reviewed systematically. Further, a detailed comparative performance analysis has been presented between four CNN models namely 1D CNN, 2D CNN, 3D CNN, and feature fusion based CNN (FFCNN). Four benchmark HSI datasets have been used in our experiment for evaluating the performance. Finally, we concluded the paper with challenges on CNN based HSI classification and future guidelines that may help the researchers to work on HSI classification using CNN. 

https://www.techscience.com/CMES/online/detail/18731/pdf

Somenath Bera

Papers

Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification

Effect of pooling strategy on convolutional neural network for classification of hyperspectral remote sensing images

Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review