Multiple instance dense connected convolution neural network for aerial image scene classification

TL;DR

This paper introduces MIDCCNN, a deep learning model that combines dense connected CNNs with attention-based multiple instance pooling to improve aerial image scene classification by preserving local features and spatial information.

Contribution

The paper proposes a novel end-to-end MIDCCNN architecture that enhances local feature preservation and spatial semantics in aerial image classification.

Findings

Outperforms existing methods on three aerial datasets

Preserves low and middle level features effectively

Highlights local semantics through attention pooling

Abstract

With the development of deep learning, many state-of-the-art natural image scene classification methods have demonstrated impressive performance. While the current convolution neural network tends to extract global features and global semantic information in a scene, the geo-spatial objects can be located at anywhere in an aerial image scene and their spatial arrangement tends to be more complicated. One possible solution is to preserve more local semantic information and enhance feature propagation. In this paper, an end to end multiple instance dense connected convolution neural network (MIDCCNN) is proposed for aerial image scene classification. First, a 23 layer dense connected convolution neural network (DCCNN) is built and served as a backbone to extract convolution features. It is capable of preserving middle and low level convolution features. Then, an attention based multiple instance pooling is proposed to highlight the local semantics in an aerial image scene. Finally, we minimize the loss between the bag-level predictions and the ground truth labels so that the whole framework can be trained directly. Experiments on three aerial image datasets demonstrate that our proposed methods can outperform current baselines by a large margin.