Abstract
Scene classification is an important and challenging task employed toward understanding remote sensing images. Convolutional neural networks have been widely applied in remote sensing scene classification in recent years, boosting classification accuracy. However, with improvements in resolution, the categories of remote sensing images have become ever more fine-grained. The high intraclass diversity and interclass similarity are the main characteristics that differentiate remote scene image classification from natural image classification. To extract discriminative representation from images, we propose an end-to-end feature fusion method that aggregates features from dual paths (AFDP). First, lightweight convolutional neural networks with fewer parameters and calculations are used to construct a feature extractor with dual branches. Then, in the feature fusion stage, a novel feature fusion method that integrates the concepts of bilinear pooling and feature connection is adopted to learn discriminative features from images. The AFDP method was evaluated on three public remote sensing image benchmarks. The experimental results indicate that the AFDP method outperforms current state-of-the-art methods, with advantages of simple form, strong versatility, fewer parameters, and less calculation.
Highlights
The development of satellite technology and Earth observation systems in recent years has led to the capturing of massive amounts of remote sensing images
DUAL BACKBONES FOR FEATURE EXTRACTION AND FEATURE TRANSFORMATION A complete convolutional neural networks (CNNs) for image classification consists of convolutional layers, pooling layers, and fully connected layers
The ablation experiments show that the designed feature fusion method is effective on multiple different CNNs, and we provide a novel baseline for module
Summary
The development of satellite technology and Earth observation systems in recent years has led to the capturing of massive amounts of remote sensing images. Owing to the top view of images and the variance in resolution, objects in the remote sensing imagery have multiple orientations and scales These factors present significant challenges to the accurate classification of remote sensing image scenes. When the categories of remote sensing images are relatively simple and distinguishable, these methods are capable of obtaining good results They have a weak ability to deal with high-resolution remote sensing images with diverse and fine-grained categories. The existence of these factors leads directly to difficulties in extracting strong robust and discriminative features with general CNNs. Based on the above discussion, to extract robust and distinguishable features from remote sensing images, we designed a novel end-to-end feature fusion method for remote sensing image scene classification with the idea of low-rank bilinear pooling [20] and feature connection.
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