Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning

Teerapong Panboonyuen,Peerapon Vateekul,Panu Srestasathiern,Kulsawasd Jitkajornwanich,Siam Lawawirojwong

doi:10.3390/rs11010083

Abstract

In the remote sensing domain, it is crucial to complete semantic segmentation on the raster images, e.g., river, building, forest, etc., on raster images. A deep convolutional encoder–decoder (DCED) network is the state-of-the-art semantic segmentation method for remotely sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose applying a recent CNN called a global convolutional network (GCN), since it can capture different resolutions by extracting multi-scale features from different stages of the network. Additionally, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, “channel attention” is presented in our network in order to select the most discriminative filters (features). Third, “domain-specific transfer learning” is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given datasets: (i) medium resolution data collected from Landsat-8 satellite and (ii) very high resolution data called the ISPRS Vaihingen Challenge Dataset. The results show that our networks outperformed DCED in terms of F 1 for 17.48% and 2.49% on medium and very high resolution corpora, respectively.

Highlights

Semantic segmentation of earthly objects such as agriculture fields, forests, roads, and urban and water areas from remotely sensed images has been manipulated in many applications in various domains, e.g., urban planning, map updates, route optimization, and navigation [1,2,3,4,5], allowing us to better understand the domain’s images and create important real-world applications.A deep convolutional neural network (CNN) is a well-known method for automatic feature learning
The results showed that our method outperforms the baseline including deep convolutional encoder–decoder (DCED) in terms of F1 and by mean of class-wise intersection over union
An experiment was conducted on the Landsat-8 corpus, and the result is shown in Tables 2 and 3 by comparing between baseline and variations of the proposed techniques

Summary

Introduction

A deep convolutional neural network (CNN) is a well-known method for automatic feature learning It can mechanically learn features at different levels and abstractions from raw images by multiple hierarchical stacking convolution and pooling layers [4,5,6,7,8,9,10,11,12,13,14]. To accomplish such a challenging task, features at different levels are required. Different numbers of layers will affect the performance of deep learning models

Objectives

Results

Conclusion