Evolving Block-Based Convolutional Neural Network for Hyperspectral Image Classification

Deep convolutional neural network (CNN) shows excellent effectiveness on hyperspectral image (HSI) classification. However, the architecture design of CNN requires abundant expert knowledge and experience, which poses great prohibition to its wide application in real-world engineering. To alleviate the issue, this article proposes an evolving block-based CNN (EB-CNN) to search the optimal architecture based on the genetic algorithm (GA) automatically. Specifically, two kinds of basic blocks with totally six different configurations are first designed to construct the search space. Then, a flexible encoding strategy is devised for the GA to allow different chromosomes to evolve with different lengths. In this manner, the width of each layer and the depth of the architecture can be simultaneously optimized. Furthermore, a novel swapping mutation operator is proposed for the GA to speed up the search efficiency and save computing resources. With the abovementioned techniques, the proposed algorithm automatically seeks the optimal CNN architecture for HSI classification, leading to its better usability than handcrafted CNNs. At last, extensive experiments conducted on five commonly used HSI datasets demonstrate that the proposed EB-CNN achieves highly competitive or even better performance, as compared with the state-of-the-art peer algorithms.