SSDANet: Spectral-Spatial Three-Dimensional Convolutional Neural Network for Hyperspectral Image Classification

Abstract

Recently, the classification of hyperspectral images has made great process. Especially, the classification methods based on three-dimensional convolutional neural network have remarkable performance due to the uniqueness of hyperspectral images. However, the hyperspectral classification still faces great challenges due to a series of problems such as the insufficient extraction of spectral-spatial features, the lack of labeled samples, the large amount of noise, the tendency of overfitting and so on. Therefore, SSDANet is proposed to solve the above problems and promote the further development of hyperspectral classification technology based on deep learning. SSDANet is a spectral-spatial three-dimensional convolutional neural network with a deep and wide structure that can significantly improve classification performance. In SSDANet, the spectral-spatial dense connectivity is put forward to protect the integrity of information. It is made up of the spectral branch and the spatial branch, which can learn and reuse the spectralspatial features. Besides, the spectral-spatial attention mechanism is proposed to adapt the special structure of hyperspectral images. It can excite important spectral-spatial information and suppress unimportant spectral-spatial information. In addition, a series of optimization methods including data augmentation, batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To verify the performance of SSDANet, experiments were implemented on two widely used datasets-Pavia University and Indian Pines. Under the condition of limited labeled samples, the classification evaluation indexes of OA, AA, and Kappa on the two datasets all exceeded 99%, reaching state-of-the-art performance. INDEX TERMS artificial intelligence, hyperspectral imaging, image processing, pattern recognition, remote sensing

Decision Tree Learning with Spatial Modal Logics

“…Although it proved fruitful in the past, classifying a pixel solely according to its spectral footprint is limited, as it disregards the spatial structure of the image. In recent times there have been many neural network-based applications that can capture spatial, inter-pixel dependencies in some sense [3,14,24,40,42,52]. These approaches leverage intrinsic spatial localities through the engineering of spatial features, and their performances are ultimately better than those of previous attempts; however, as we have already observed, as a general rule functional approaches give up the interpretability and the explainability of the resulting models.…”

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

“…These reasons made researchers struggle to analyze properly, process, and classify HSIs. On the contrary, the advancements of ML/DL technologies have opened a broad gateway of research that researchers are still exploring and combining with different groupings to address the HSI classification problem in real life, dealing with the limitations mentioned above [ 26 , 131 ]. The tabular depiction of the advantages and disadvantages of the ML and non-ML strategies applied for HSI classification is shown in Table 13.…”

A Decompressed Spectral-Spatial Multiscale Semantic Feature Network for Hyperspectral Image Classification

“…Owning to the abundant spatial and spectral information, HSI plays an important role in many practical applications, such as urban planning, land-cover investigation, precision agriculture, military detection, and environmental monitoring [5][6][7][8][9]. In the last few decades, HSI classification has been one of the most popular research fields [10] and attracted multitudinous scholars to devote great efforts to improving classification accuracy [11][12][13][14][15].…”