Spectral–spatial classification of hyperspectral images using deep convolutional neural networks

Abstract

In this letter, a novel deep learning framework for hyperspectral image classification using both spectral and spatial features is presented. The framework is a hybrid of principal component analysis, deep convolutional neural networks (DCNNs) and logistic regression (LR). The DCNNs for hierarchically extract deep features is introduced into hyperspectral image classification for the first time. The proposed technique consists of two steps. First, feature map generation algorithm is presented to generate the spectral and spatial feature maps. Second, the DCNNs-LR classifier is trained to get useful high-level features and to fine-tune the whole model. Comparative experiments conducted over widely used hyperspectral data indicate that DCNNs-LR classifier built in this proposed deep learning framework provides better classification accuracy than previous hyperspectral classification methods.

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

“…In order to evaluate the efficacy of the proposed 3D-CNN method, we compared it with three deep learning HSI classification approaches: SAE-LR [29], DBN-LR [31], and 2D-CNN [33]. Overall accuracy (OA), average accuracy (AA) and kappa statistic (K) were adopted to assess the classification performance of each model.…”

“…Section 2 first provides an introduction to the relevant background, and then presents our 3D-CNN-based HSI classification framework. We describe the datasets and experimental setup in Section 3 and discusses the experimental results in Section 4, empirically comparing the proposed method with three other deep learning-based HSI classification approaches-namely SAE-LR (logistic regression) [29], DBN-LR [31] and 2D-CNN [33]. Finally, we summarize the work and conclude this paper in Section 5.…”

“…In order to deal with this problem, DR methods are usually applied to reduce the spectral dimensionality prior to 2D-CNN being employed for feature extraction and classification [33][34][35]. For instance, in [33], the first three principal components (PCs) are extracted from HSI by PCA, and then a 2D-CNN is used to extract deep features from condensed HSI with a window size of 42 × 42 in order to predict the label of each pixel.…”

“…For instance, in [33], the first three principal components (PCs) are extracted from HSI by PCA, and then a 2D-CNN is used to extract deep features from condensed HSI with a window size of 42 × 42 in order to predict the label of each pixel. Randomized PCA (R-PCA) was also introduced along the spectral dimension to compress the entire HSI in [34], with the first 10 or 30 PCs being retained.…”

Four Decades of Estuarine Wetland Changes in the Yellow River Delta Based on Landsat Observations Between 1973 and 2013

“…The technical errors were mainly caused by the characteristics of the wetlands, such as changes in different season, they have complex spectra, they are heterogeneous, and the same land cover types have multiple spectrums. To improve the classification accuracy in the future, more research can be done on the following aspects: (1) To discover more effective features, not just in spectrum, new technologic methods maybe good alternative choices (e.g., synthetic aperture radar, lidar, and geospatial modeling); (2) to enhance the intensity of machine learning; taking into account the all possible situations via the new learning structures (e.g., deep convolutional artificial neural network (ANN) and deep learning) [104][105][106][107][108][109][110][111][112]. The deep convolutional neural network algorithm, in particular, has better learning and generalization performance for multiple variables and large datasets.…”