Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images

TL;DR

This paper introduces DDCM-Net, a deep learning model utilizing dense dilated convolutions for accurate semantic mapping of high-resolution remote sensing images, effectively recognizing complex objects at multiple scales.

Contribution

The paper presents a novel DDCM-Net architecture that merges dense dilated convolutions with varying dilation rates to enhance multi-scale object recognition in remote sensing images.

Findings

Achieved 92.3% F1-score on ISPRS Potsdam dataset using RGB bands.

Obtained 89.8% F1-score on ISPRS Vaihingen dataset with IRRG bands.

Outperformed previous state-of-the-art methods in semantic mapping accuracy.

Abstract

We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and complex shaped objects with similar color and textures, such as buildings, surfaces/roads, and trees in very high resolution remote sensing images. The proposed DDCM-Net consists of dense dilated convolutions merged with varying dilation rates. This can effectively enlarge the kernels' receptive fields, and, more importantly, obtain fused local and global context information to promote surrounding discriminative capability. We demonstrate the effectiveness of the proposed DDCM-Net on the publicly available ISPRS Potsdam dataset and achieve a performance of 92.3% F1-score and 86.0% mean intersection over union accuracy by only using the RGB bands, without any post-processing. We also show results on the ISPRS Vaihingen dataset, where the DDCM-Net trained with IRRG bands, also obtained better mapping accuracy (89.8% F1-score) than previous state-of-the-art approaches.