A Novel Framework for Remote Sensing Image Scene Classification

Abstract

ABSTRACT:High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57% and 83.35% respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

Automatic Object Extraction From High Resolution Aerial Imagery With Simple Linear Iterative Clustering and Convolutional Neural Networks

“…The dataset was divided into 80% for training and 20% for validation, and the selected CNN model achieved 96.7% accuracy. This result is similar to the accuracy achieved with the framework proposed in Jiang et al (2018). An external validation was conducted with image samples collected with different sensors in other years, and the details are described in Section 5.…”

“…There were two main breakthroughs on the algorithm side of the model: (i) the adoption of a simpler activation function (the rectified linear unit -ReLU), which, according to Glorot et al (2010), can speed up the training process, aiming at faster convergence; and (ii) the adoption of the dropout strategy (Hilton et al, 2012) to minimize the effects of overfitting. According to Jiang et al (2018), the ReLU function is:…”

Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017

“…XGBoost is a new implementation of boosting CART algorithms that improves the accuracy of classification through iterative computation of weak (basic) classifiers. It usually outperforms benchmark classifiers such as support vector machines (SVM) or RF, and thus has gained much popularity in the Machine Learning community-particularly in Kaggle competitions, and most recently in image classification [32][33][34][35]. In identifying important features, the XGBoost algorithm uses boosted trees to obtain feature scores.…”