Orthogonal Equations of Multi-Spectral Satellite Imagery for the Identification of Un-Excavated Archaeological Sites

Αγαπίου, Άθως; Alexakis, Dimitrios D.; Sarris, Apostolos; Hadjimitsis, Diofantos G.

doi:10.3390/rs5126560

Cited by 43 publications

(29 citation statements)

References 36 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Archaeological traces have similar features in imagery such as variations in shadowing, soil color, moisture patterning, frost and snow marks, and crop marks [15,20]. In the literature, most methods focus on applying radiometric enhancement, spatial filters and spectral indexes for archaeological investigations [15,[36][37][38]. In order to save time and manpower, several studies have tried to employ (semi-) automatic methods to extract archaeological traces [14,16,21,26,[39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

et al. 2014

View full text Add to dashboard Cite

Qanats in northern Xinjiang of China provide valuable information for agriculturists and anthropologists who seek fundamental understanding of the distribution of qanat water supply systems with regard to water resource utilization, the development of oasis agriculture, and eventually climate change. Only the tops of qanat shafts (TQSs), indicating the course of the qanats, can be observed from space, and their circular archaeological traces can also be seen in very high resolution imagery in Google Earth. The small size of the TQSs, vast search regions, and degraded features make manually extracting them from remote sensing images difficult and costly. This paper proposes an automated TQS extraction method that adopts mathematical morphological processing methods before an edge detecting module is used in the circular Hough transform approach. The accuracy OPEN ACCESSRemote Sens. 2014, 6 11957 assessment criteria for the proposed method include: (i) extraction percentage (E) = 95.9%, branch factor (B) = 0 and quality percentage (Q) = 95.9% in Site 1; and (ii) extraction percentage (E) = 83.4%, branch factor (B) = 0.058 and quality percentage (Q) = 79.5% in Site 2. Compared with the standard circular Hough transform, the quality percentages (Q) of our proposed method were improved to 95.9% and 79.5% from 86.3% and 65.8% in test sites 1 and 2, respectively. The results demonstrate that wide-area discovery and mapping can be performed much more effectively based on our proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

et al. 2014

View full text Add to dashboard Cite

show abstract

“…New linear orthogonal equations for the enhancement of crop marks, for several existing medium and high resolution satellite sensors have been recently proposed by the authors (Agapiou et al 2013b). These linear transformations are based on the physical characteristics of crop marks, vegetation and soil.…”

Section: New Orthogonal Equations For Detection Of Crop Marksmentioning

confidence: 99%

Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology

Themistocleous

Αγαπίου

Cuca

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Commission VI, WG VI/4 KEY WORDS: remote sensing archaeology, UAV, ground spectroscopy, crop marks, cultural heritage ABSTRACT:Remote sensing has open up new dimensions in archaeological research. Although there has been significant progress in increasing the resolution of space/aerial sensors and image processing, the detection of the crop (and soil marks) formations, which relate to buried archaeological remains, are difficult to detect since these marks may not be visible in the images if observed over different period or at different spatial/spectral resolution. In order to support the improvement of earth observation remote sensing technologies specifically targeting archaeological research, a better understanding of the crop/soil marks formation needs to be studied in detail. In this paper the contribution of both Unmanned Aerial Systems as well ground spectroradiometers is discussed in a variety of examples applied in the eastern Mediterranean region (Cyprus and Greece) as well in Central Europe (Hungary). In-situ spectroradiometric campaigns can be applied for the removal of atmospheric impact to simultaneous satellite overpass images. In addition, as shown in this paper, the systematic collection of ground truth data prior to the satellite/aerial acquisition can be used to detect the optimum temporal and spectral resolution for the detection of stress vegetation related to buried archaeological remains. Moreover, phenological studies of the crops from the area of interest can be simulated to the potential sensors based on their Relative Response Filters and therefore prepare better the satellite-aerial campaigns. Ground data and the use of Unmanned Aerial Systems (UAS) can provide an increased insight for studying the formation of crop and soil marks. New algorithms such as vegetation indices and linear orthogonal equations for the enhancement of crop marks can be developed based on the specific spectral characteristics of the area. As well, UAS can be used for remote sensing applications in order to document, survey and model cultural heritage and archaeological sites.

show abstract

“…Different types of remote sensing imagery have been used including aerial photographs [1,2], spaceborne optical images [3][4][5][6], spaceborne and airborne RADAR images [7], airborne LIDAR images [8,9], airborne imaging spectroscopy and hyperspectral imagery [10,11]. In the literature, much work has been done to develop automatic algorithms to support the digitalization of man-made structures such as buildings and roads [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

et al. 2015

View full text Add to dashboard Cite

This paper considers the problem of detecting archaeological traces in digital aerial images by analyzing the pixel variance over regions around selected points. In order to decide if a point belongs to an archaeological trace or not, its surrounding regions are considered. The one-way ANalysis Of VAriance (ANOVA) is applied several times to detect the differences among these regions; in particular the expected shape of the mark to be detected is used in each region. Furthermore, an effect size parameter is defined by comparing the statistics of these regions with the statistics of the entire population in order to measure how strongly the trace is appreciable. Experiments on synthetic and real images demonstrate the effectiveness of the proposed approach with respect to some state-of-the-art methodologies.

show abstract

Orthogonal Equations of Multi-Spectral Satellite Imagery for the Identification of Un-Excavated Archaeological Sites

Cited by 43 publications

References 36 publications

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

Contact Info

Product

Resources

About