Evaluation of Landsat 8 OLI/TIRS Level-2 and Sentinel 2 Level-1C Fusion Techniques Intended for Image Segmentation of Archaeological Landscapes and Proxies

Athos Agapiou

doi:10.3390/rs12030579

Abstract

The use of medium resolution, open access, and freely distributed satellite images, such as those of Landsat, is still understudied in the domain of archaeological research, mainly due to restrictions of spatial resolution. This investigation aims to showcase how the synergistic use of Landsat and Sentinel optical sensors can efficiently support archaeological research through object-based image analysis (OBIA), a relatively new scientific trend, as highlighted in the relevant literature, in the domain of remote sensing archaeology. Initially, the fusion of a 30 m spatial resolution Landsat 8 OLI/TIRS Level-2 and a 10 m spatial resolution Sentinel 2 Level-1C optical images, over the archaeological site of “Nea Paphos” in Cyprus, are evaluated in order to improve the spatial resolution of the Landsat image. At this step, various known fusion models are implemented and evaluated, namely Gram–Schmidt, Brovey, principal component analysis (PCA), and hue-saturation-value (HSV) algorithms. In addition, all four 10 m available spectral bands of the Sentinel 2 sensor, namely the blue, green, red, and near-infrared bands (Bands 2 to 4 and Band 8, respectively) were assessed for each of the different fusion models. On the basis of these findings, the next step of the study, focused on the image segmentation process, through the evaluation of different scale factors. The segmentation process is an important step moving from pixel-based to object-based image analysis. The overall results show that the Gram–Schmidt fusion method based on the near-infrared band of the Sentinel 2 (Band 8) at a range of scale factor segmentation to 70 are the optimum parameters for the detection of standing visible monuments, monitoring excavated areas, and detecting buried archaeological remains, without any significant spectral distortion of the original Landsat image. The new 10 m fused Landsat 8 image provides further spatial details of the archaeological site and depicts, through the segmentation process, important details within the landscape under examination.

Highlights

IntroductionObject-based image analysis (OBIA) builds on widely used remote sensing techniques such as image segmentation, edge detection, feature extraction, and classification concepts, having been originally developed as a result of the recognition of limitations working with pixel-based image approaches [1,2]
The fusion of a 30 m spatial resolution Landsat 8 OLI/TIRS Level-2 and a 10 m spatial resolution Sentinel 2 Level-1C optical images, over the archaeological site of “Nea Paphos” in Cyprus, are evaluated in order to improve the spatial resolution of the Landsat image
Object-based image analysis (OBIA) builds on widely used remote sensing techniques such as image segmentation, edge detection, feature extraction, and classification concepts, having been originally developed as a result of the recognition of limitations working with pixel-based image approaches [1,2]

Summary

Introduction

Object-based image analysis (OBIA) builds on widely used remote sensing techniques such as image segmentation, edge detection, feature extraction, and classification concepts, having been originally developed as a result of the recognition of limitations working with pixel-based image approaches [1,2]. A workflow of an archaeological object-based image analysis designed for stimulating the development of an operational routine for object-based applications in archaeology was recently proposed by [8]. OBIA analysis was introduced for archaeological research as part of an image fusion technique to combine different prospection datasets [10]. Integration and fusion of various datasets should be an active area for future research in archaeological research [11,12], while the development of new approaches is considered a key, but difficult, topic that requires more investigation [9]

Objectives

Methods

Results