Multispectral Semantic Segmentation for Land Cover Classification: An Overview

Abstract

Land cover classification (LCC) is a process used to categorize the earth's surface into distinct land types. This classification is vital for environmental conservation, urban planning, agricultural management, and climate change research, providing essential data for sustainable decision making. The use of multispectral imaging (MSI), which captures data beyond the visible spectrum, has emerged as one of the most utilized image modalities for addressing this task. In addition, semantic segmentation techniques play a vital role in this domain, enabling the precise delineation and labeling of land cover classes within imagery. The integration of these three concepts has given rise to an intriguing and ever-evolving research field, witnessing continuous advancements aimed at enhancing multispectral semantic segmentation (MSSS) methods for LCC. Given the dynamic nature of this field, there is a need for a thorough examination of the latest trends and advancements to understand its evolving landscape. Therefore, this article presents a review of current aspects in the field of MSSS for LCC, addressing the following key points: 1) prevalent datasets and data acquisition methods; 2) preprocessing methods for managing MSI data; 3) typical metrics and evaluation criteria used for assessing performance of methods; 4) current techniques and methodologies employed; and 5) spectral bands beyond the visible spectrum commonly utilized. Through this analysis, our objective is to provide valuable insights into the current state of MSSS for LCC, contributing to the ongoing development and understanding of this dynamic field while also providing perspectives for future research directions.

Keywords

• Computer vision (CV)
• deep learning (DL)
• image segmentation
• land cover classification (LCC)
• multispectral imaging (MSI)
• semantic segmentation