Abstract
Hyperspectral image (HSI) classification is one of the most challenging problems in understanding HSI. Convolutional neural network(CNN), with the strong ability to extract features using the hidden layers in the network, has been introduced to solve this problem. However, several fully connected layers are always appended at the end of CNN, which dramatically reduced the efficiency of space utilization and make the classification algorithm hard to converge. Recently, a new network architecture called capsule network (CapsNet) was presented to improve the CNN. It uses groups of neurons as capsules to replace the neurons in traditional neural networks. Since the capsule can provide superior spectral features and spatial information extracted, its performance is better than the most advanced CNN in some fields. Motivated by this idea, a new remote sensing hyperspectral image classification algorithm called Conv-Caps is proposed to make full use of the advantages of both. We integrate spectral and spatial information into the proposed framework and combine Conv-Caps with Markov Random Field (MRF), which uses the graph cut expansion method to solve the classification task. The Caps-MRF method is further proposed. First, select an initial feature extractor,which a CNN without fully connected layers. Then, the initial recognition feature map is put into the newly designed CapsNet to obtain the probability map. Finally, the MRF model is used to calculate the subdivision labels. The presented method is trained with three real HSI datasets and is compared with the latest methods. We find the framework can produce competitive classification performance.
Highlights
W ITH technology advancing, various types of highresolution Earth surface images become readily available [1]
Vector capsule vj represents a category of the hyperspectral image, and the modulus of the vector represents the probability of the category
For the Pavia Centre (PC) dataset, by comparing the real ground reference with the classification map, we found that the classification results produced by the Support Vector Machine (SVM) model are very noisy
Summary
W ITH technology advancing, various types of highresolution Earth surface images become readily available [1]. Hyperspectral images contain abundant spectral and spatial information [2]. Hyperspectral image classification aims at automatically assigning a specific semantic label to each pixel according to its spatial-spectral information [3]. CapsNet splits the hyperspectral image into vector capsules Ii through a convolutional step. Compared with CNN, capsule networks have some disadvantages, such as being unsuitable for large databases and slower execution. To overcome these shortcomings, Paoletti et al [13] propose a new CNN architecture based on spectral-spatial capsule networks while significantly reduce the network design complexity. Yin et al [21] tune a new CapsNet architecture with three convolutional layers and achieve superior performance in HSI classification to the CNN-based methods. Wang et al [22] resolve the problem that high resolution may increase intraclass difference and interclass similarity with CapsTripleGAN
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