Abstract

Due to the advent of powerful parallel processing tools, including modern Graphics Processing Units (GPU), new deep learning algorithms, such as Convolutional Neural Networks (CNNs), have significantly altered the state-of-the-art algorithms in satellite classification of complex environments. Recent studies have demonstrated that the generic feature maps extracted from CNNs are incredibly effective in wetland classification. The main drawback of very deep CNNs is described as structurally complex, causing the need for extensive training data. To address deep Convolutional Neural Network’s limitations, a timely and computationally efficient CNN architecture is proposed in this paper. The results of the proposed model were compared to other well-known CNNs (i.e., GoogleNet and SqueezeNet) and several machine learning algorithms, including Random Forest, Gaussian Naïve Bayes, and the Bayesian Optimized Tree. Results showed while significantly reduced the training time, the proposed deep learning method outperformed GoogleNet and SqueezeNet by about 12.71% and 12.2% in terms of mean overall accuracy, respectively. The classification results shown that the accuracy of wetland classes (fen, marsh, swamp, and shallow water) were significantly improved by applying the proposed CNN method.

摘要

由于强大的并行处理工具的出现，包括现代图形处理单元 (GPU)，新的深度学习算法，如卷积神经网络 (CNN)，已经显着改变了复杂卫星分类中的最先进算法。环境。最近的研究表明，从 CNN 中提取的通用特征图在湿地分类中非常有效。非常深的 CNN 的主要缺点被描述为结构复杂，导致需要大量的训练数据。为了解决深度卷积神经网络的局限性，本文提出了一种及时且计算效率高的 CNN 架构。将所提出模型的结果与其他著名的 CNN（即 GoogleNet 和 SqueezeNet）和几种机器学习算法进行了比较，包括随机森林、高斯朴素贝叶斯和贝叶斯优化树。结果表明，虽然显着减少了训练时间，但所提出的深度学习方法在平均总体准确率方面分别优于 GoogleNet 和 SqueezeNet 约 12.71% 和 12.2%。分类结果表明，应用所提出的 CNN 方法显着提高了湿地类别（沼泽、沼泽、沼泽和浅水）的准确性。