MultiScale Probability Map guided Index Pooling with Attention-based learning for Road and Building Segmentation

TL;DR

This paper introduces MSSDMPA-Net, a novel attention-based deep learning framework that significantly improves the accuracy of road and building segmentation from satellite images by preserving geometric and multi-scale features.

Contribution

The paper proposes two new modules, DAMIP and DAMSCA, along with dilated convolution and multi-scale supervision, to enhance segmentation accuracy over existing CNN-based methods.

Findings

Achieves state-of-the-art performance on multiple benchmark datasets.

Effectively preserves geometric information and multi-scale features.

Outperforms existing methods in building and road segmentation accuracy.

Abstract

Efficient road and building footprint extraction from satellite images are predominant in many remote sensing applications. However, precise segmentation map extraction is quite challenging due to the diverse building structures camouflaged by trees, similar spectral responses between the roads and buildings, and occlusions by heterogeneous traffic over the roads. Existing convolutional neural network (CNN)-based methods focus on either enriched spatial semantics learning for the building extraction or the fine-grained road topology extraction. The profound semantic information loss due to the traditional pooling mechanisms in CNN generates fragmented and disconnected road maps and poorly segmented boundaries for the densely spaced small buildings in complex surroundings. In this paper, we propose a novel attention-aware segmentation framework, Multi-Scale Supervised Dilated Multiple-Path Attention Network (MSSDMPA-Net), equipped with two new modules Dynamic Attention Map Guided Index Pooling (DAMIP) and Dynamic Attention Map Guided Spatial and Channel Attention (DAMSCA) to precisely extract the building footprints and road maps from remotely sensed images. DAMIP mines the salient features by employing a novel index pooling mechanism to retain important geometric information. On the other hand, DAMSCA simultaneously extracts the multi-scale spatial and spectral features. Besides, using dilated convolution and multi-scale deep supervision in optimizing MSSDMPA-Net helps achieve stellar performance. Experimental results over multiple benchmark building and road extraction datasets, ensures MSSDMPA-Net as the state-of-the-art (SOTA) method for building and road extraction.