Satellite Image Scene Classification via ConvNet with Context Aggregation

TL;DR

This paper introduces a two-pathway ResNet architecture with context aggregation for satellite image scene classification, improving accuracy by modeling both local details and regional context.

Contribution

It proposes a novel two-pathway ResNet with context aggregation for enhanced satellite scene classification performance.

Findings

Achieves better accuracy than state-of-the-art methods on UCM Land Use dataset.

Demonstrates significant improvements on NWPU-RESISC45 dataset.

Effectively models local and regional features for scene understanding.

Abstract

Scene classification is a fundamental problem to understand the high-resolution remote sensing imagery. Recently, convolutional neural network (ConvNet) has achieved remarkable performance in different tasks, and significant efforts have been made to develop various representations for satellite image scene classification. In this paper, we present a novel representation based on a ConvNet with context aggregation. The proposed two-pathway ResNet (ResNet-TP) architecture adopts the ResNet as backbone, and the two pathways allow the network to model both local details and regional context. The ResNet-TP based representation is generated by global average pooling on the last convolutional layers from both pathways. Experiments on two scene classification datasets, UCM Land Use and NWPU-RESISC45, show that the proposed mechanism achieves promising improvements over state-of-the-art methods.