A comparison of four relative radiometric normalization (RRN) techniques for mosaicing H-res multi-temporal thermal infrared (TIR) flight-lines of a complex urban scene

Rahman, Mir Mustafizur; Hay, Geoffrey J.; Couloigner, Isabelle; Hemachandran, Bharanidharan; Bailin, Jeremy

doi:10.1016/j.isprsjprs.2015.05.002

Cited by 31 publications

(24 citation statements)

References 36 publications

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“…A manual selection of high-quality pseudo-invariant features as suggested in Rahman et al (2015) is considered not feasible with regards to the formulated goal of integrating the algorithm in an automated processing chain. Therefore the algorithm is developed to automatically detect appropriate pixels and writing them to a PIF mask image including a quality measure 0 128 255…”

Section: Pseudo-invariant Feature Generationmentioning

confidence: 99%

An Enhanced Algorithm for Automatic Radiometric Harmonization of High-Resolution Optical Satellite Imagery Using Pseudoinvariant Features and Linear Regression

Langheinrich

Fischer

Probeck

et al. 2017

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

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ABSTRACT:The growing number of available optical remote sensing data providing large spatial and temporal coverage enables the coherent and gapless observation of the earth's surface on the scale of whole countries or continents. To produce datasets of that size, individual satellite scenes have to be stitched together forming so-called mosaics. Here the problem arises that the different images feature varying radiometric properties depending on the momentary acquisition conditions. The interpretation of optical remote sensing data is to a great extent based on the analysis of the spectral composition of an observed surface reflection. Therefore the normalization of all images included in a large image mosaic is necessary to ensure consistent results concerning the application of procedures to the whole dataset. In this work an algorithm is described which enables the automated spectral harmonization of satellite images to a reference scene. As the stable and satisfying functionality of the proposed algorithm was already put to operational use to process a high number of SPOT-4/-5, IRS LISS-III and Landsat-5 scenes in the frame of the European Environment Agency's Copernicus/GMES Initial Operations (GIO) High-Resolution Layer (HRL) mapping of the HRL Forest for 20 Western, Central and (South)Eastern European countries, it is further evaluated on its reliability concerning the application to newer Sentinel-2 multispectral imaging products. The results show that the algorithm is comparably efficient for the processing of satellite image data from sources other than the sensor configurations it was originally designed for.

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Section: Pseudo-invariant Feature Generationmentioning

confidence: 99%

An Enhanced Algorithm for Automatic Radiometric Harmonization of High-Resolution Optical Satellite Imagery Using Pseudoinvariant Features and Linear Regression

Langheinrich

Fischer

Probeck

et al. 2017

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

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“…Moreover, the environment is in a state of thermal equilibrium during this period. Prior to analysis, the following image pre-processing procedures were applied: (I) Object-Based Mosaicing (OBM) to mosaic around flight line joins [10]; (II) Thermal Urban Road Normalization (TURN) to reduce microclimatic variability within flight lines [11]; and (III) Relative Radiometric Normalization (RRN) to normalize the radiometric properties of multiple flight lines so that the final scene appears as if it were acquired under the same environmental conditions and at the same time [36].…”

Section: Study Area and Datamentioning

confidence: 99%

Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API

2014

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Abstract:The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, Geographic Object-Based Image Analysis (GEOBIA), and a Geoweb environment to allow the residents of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and the city. To ensure the accuracy of these measures, the correct emissivity of roof materials needs to be known. However, roof material information is not readily available in the Canadian public domain. To overcome this challenge, a unique Volunteered Geographic Information (VGI) application was developed using Google Street View that engages citizens to classify the roof materials of single dwelling residences in a simple and intuitive manner. Since data credibility, quality, and accuracy are major concerns when using VGI, a private Multiple Listing Services (MLS) dataset was used for cross-verification. From May-November 2013, 1244 volunteers from 85 cities and 14 countries classified 1815 roofs in the study area. Results show (I) a 72% match between the VGI and MLS data; and (II) in the majority of cases, roofs with greater than, or equal to five contributions have the same material defined in both datasets. Additionally, this research meets new challenges to the GEOBIA community to incorporate existing GIS vector data within an object-based workflow and engages the public to provide volunteered information for urban objects from which new geo-intelligence is created in support of urban energy efficiency.

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“…The simplest way to perform HM is to create the histogram of the master and the slave images, then calculate the mean difference using Equation (1) and use it to shift (a.k.a. normalize) the slave histogram to the master [13]. Figure 2 displays a hypothetical example of the HM technique.…”

Section: Histogram Matchingmentioning

confidence: 99%

“…Scheidt et al [12] mosaicked night-time ASTER TIR data by automatically selecting pseudo-invariant features (PIFs) from scene overlaps and then fitting the PIFs in a linear regression model. More recently, Rahman et al [13] recognized the need to validate RRN techniques on high resolution (H-res) multi-temporal TIR imagery and evaluated four RRN techniques (typically used for multispectral data) on multiple flight lines of TABI-1800 (Thermal Airborne Broadband Imager) data (at a 50-cm spatial resolution). These included: (i) histogram matching; (ii) pseudo-invariant feature (PIF)-based linear regression; (iii) PIF-based Theil-Sen regression (the Theil-Sen [14,15] estimator is a robust linear regression model that uses the median of pairwise slopes as an estimator of the slope parameter of the correlation between two datasets [16]); and (iv) no-change stratified random sample (NCSRS)-based linear regression.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, we hypothesize that non-linear RRN techniques should demonstrate increased radiometric agreement over complex urban surfaces, compared to similar linear techniques. To test this hypothesis, the objective of this paper is to evaluate the two most suitable linear techniques for the RRN of airborne TIR imagery, as recently described by Rahman et al [13], against two newly proposed polynomial techniques (which are expected to better suit the thermal complexity of urban surfaces) and to evaluate them visually and statistically. To achieve this objective, the following section (Section 2) describes the study area, datasets and the RRN techniques used.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery

et al. 2014

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Thermal Infrared (TIR) remote sensing images of urban environments are increasingly available from airborne and satellite platforms. However, limited access to high-spatial resolution (H-res: ~1 m) TIR satellite images requires the use of TIR airborne sensors for mapping large complex urban surfaces, especially at micro-scales. A critical limitation of such H-res mapping is the need to acquire a large scene composed of multiple flight lines and mosaic them together. This results in the same scene components (e.g., roads, buildings, green space and water) exhibiting different temperatures in different flight lines. To mitigate these effects, linear relative radiometric normalization (RRN) techniques are often applied. However, the Earth's surface is composed of features whose thermal behaviour is characterized by complexity and non-linearity. Therefore, we hypothesize that non-linear RRN techniques should demonstrate increased radiometric agreement over similar linear techniques. To test this hypothesis, this paper evaluates four (linear and non-linear) RRN techniques, including: (i) histogram matching (HM); (ii) pseudo-invariant feature-based OPEN ACCESSRemote Sens. 2014, 6 11811 polynomial regression (PIF_Poly); (iii) no-change stratified random sample-based linear regression (NCSRS_Lin); and (iv) no-change stratified random sample-based polynomial regression (NCSRS_Poly); two of which (ii and iv) are newly proposed non-linear techniques. When applied over two adjacent flight lines (~70 km 2 ) of TABI-1800 airborne data, visual and statistical results show that both new non-linear techniques improved radiometric agreement over the previously evaluated linear techniques, with the new fully-automated method, NCSRS-based polynomial regression, providing the highest improvement in radiometric agreement between the master and the slave images, at ~56%. This is ~5% higher than the best previously evaluated linear technique (NCSRS-based linear regression).

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A comparison of four relative radiometric normalization (RRN) techniques for mosaicing H-res multi-temporal thermal infrared (TIR) flight-lines of a complex urban scene

Cited by 31 publications

References 36 publications

An Enhanced Algorithm for Automatic Radiometric Harmonization of High-Resolution Optical Satellite Imagery Using Pseudoinvariant Features and Linear Regression

An Enhanced Algorithm for Automatic Radiometric Harmonization of High-Resolution Optical Satellite Imagery Using Pseudoinvariant Features and Linear Regression

Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API

An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery

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