Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery

TL;DR

This study evaluates deep learning semantic segmentation models across multispectral, hyperspectral, and aerial imagery for land cover mapping, highlighting the effectiveness of LinkNet and multispectral data.

Contribution

It introduces a comprehensive comparison of multiple deep learning models on various image types for land cover classification, emphasizing the broad applicability of LinkNet and multispectral data.

Findings

LinkNet achieved IoU of 0.92 across datasets.

Multispectral images outperformed others with IoU 0.993 and F1-score 0.997.

The approach is open source and suitable for long-term environmental monitoring.

Abstract

In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification using satellite imageries has been explored and become more prevalent in recent years, but the methodologies remain some drawbacks of subjective and time-consuming. Some deep learning techniques have been utilized to overcome these limitations. However, most studies implemented just one image type to evaluate algorithms for land cover mapping. Therefore, our study conducted deep learning semantic segmentation in multispectral, hyperspectral, and high spatial aerial image datasets for landcover mapping. This research implemented a semantic segmentation method such as Unet, Linknet, FPN, and PSPnet for categorizing vegetation, water, and others (i.e., soil and impervious surface). The LinkNet model obtained high accuracy in IoU (Intersection Over Union) at 0.92 in all datasets, which is comparable with other mentioned techniques. In evaluation with different image types, the multispectral images showed higher performance with the IoU, and F1-score are 0.993 and 0.997, respectively. Our outcome highlighted the efficiency and broad applicability of LinkNet and multispectral image on land cover classification. This research contributes to establishing an approach on landcover segmentation via open source for long-term future application.