Making color infrared film a more effective high altitude sensor

Infrared color film for remote sensors at high altitude

An evaluation of remote sensing techniques for the detection of archaeological features

The thesis evaluates the potential for using multiple remote sensing techniques to identify archaeological features buried beneath vegetation. The research is carried out on data acquired by the Natural Environmental Research Council (henceforth NERC), who have provided multispectral, LIght Detection and Ranging (henceforth LİDAR) and high resolution colour aerial photographic data. The returns from these techniques are compared with ground-based geophysical survey data carried out by the Landscape Research Centre (henceforth LRC) on four major projects funded by English Heritage (henceforth EH), as well as aerial photographs collected by the LRC. The methodology adopted for this research uses primarily qualitative techniques, where images from different forms of remote sensing are processed, georeferenced and then compared for their ability to identify archaeological features. A process has been developed where the returns from each form of remote sensing are interpreted and then digitised as vector polygons with individual database entries for each polygon, which allows a direct comparison between the different datasets to be conducted. The timing of the data acquisition is shown to be critical if the data is to be used for small scale anomaly detection, as is the case with archaeological features. This is particularly true for airborne sensors, although the returns from ground-based geophysical surveys can also be affected if carried out under unfavourable circumstances. It was established that the different underlying drift geologies of the Vale of Pickering also affected the returns from remote sensing sources, with the calcareous and sandy zones of the southern part of the research area generally (though not exclusively) providing better results than the more alluvial zones to the north. The use of different forms of airborne remote sensing and geophysical survey are demonstrated to be complementary, with each form of remote sensing identifying different, though not always exclusively different, archaeological anomalies

The Spiral that Vanished: The Application of Non-Contact Recording Techniques to an Elusive Rock Art Motif at Castlerigg Stone Circle in Cumbria

This article describes the recording of stone 11 of the Castlerigg stone circle in Cumbria through two different non-contact techniques: laser scanning and ground-based remote sensing. Despite the unproblematic recording of modern graffiti, neither technique was able to document the spiral photographed and rubbed in 1995. It is concluded that the spiral was most probably painted and has since faded away due to natural events. The discovery and loss of of the spiral motif in Castlerigg is seen as a cautionary tale. In particular, it seems to suggest that it is time to take advantage of the novel technologies based on the digitisation of 3D surfaces with millimetre and submillimetre accuracy such as laser scanning and ground-based remote sensing. They offer many advantages to the recording of prehistoric carvings. In addition to avoiding direct contact with the rock surface eliminating the preservation concerns raised by other techniques, both produce high quality images (laser scaning offering a greater potential for this, but at higher cost) having a much higher level of objectivity, and precision and accuracy far beyond those of traditional recording methods such as wax rubbings and scale drawings

Value of remote sensing in forest surveys

Available from British Library Document Supply Centre- DSC:D42883/82 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Analysis of the quality of image data acquired by the LANDSAT-4 thematic mapper and multispectral scanners

Image products and numeric data were extracted from both TM and MSS data in an effort to evaluate the quality of these data for interpreting major agricultural resources and conditions in California's Central Valley. The utility of TM data appears excellent for meeting most of the inventory objectives of the agricultural resource specialist. These data should be extremely valuable for crop type and area proportion estimation, for updating agricultural land use survey maps at 1:24,000-scale and smaller, for field boundary definition, and for determining the size and location of individual farmsteads

Use of remote sensing techniques for geological hazard surveys in vegetated urban regions

The feasibility of using aerial photography for lithologic differentiation in a heavily vegetated region is investigated using multispectral imagery obtained from LANDSAT satellite and aircraft-borne photography. Delineating and mapping of localized vegetal zones can be accomplished by the use of remote sensing because a difference in morphology and physiology results in different natural reflectances or signatures. An investigation was made to show that these local plant zones are affected by altitude, topography, weathering, and gullying; but are controlled by lithology. Therefore, maps outlining local plant zones were used as a basis for lithologic map construction

Monitoring Western Australia\u27s rangelands

Rangelands, native pastures used for grazing domestic livestock, occupy about 100 million hectares or 40 per cent of Western Australia, extending from the tropical grasslands of the Kimberley to the arid shrub steppe of the Nullarbor Plain. The rangelands are characterized by highly variable seasonal conditions. Carrying capacity can fluctuate dramatically from year to year. Grazing management requires a tactical approach from one season to the next because of the great variation in the capacity of the land to support stock. Rangeland monitoring provides pastoralists with objective information on these changes to assist their management decision making. The Western Australian Rangeland Monitoring System (WARMS) is being developed for this purpose

Enhancing spatial resolution of remotely sensed data for mapping freshwater environments

Freshwater environments are important for ecosystem services and biodiversity. These environments are subject to many natural and anthropogenic changes, which influence their quality; therefore, regular monitoring is required for their effective management. High biotic heterogeneity, elongated land/water interaction zones, and logistic difficulties with access make field based monitoring on a large scale expensive, inconsistent and often impractical. Remote sensing (RS) is an established mapping tool that overcomes these barriers. However, complex and heterogeneous vegetation and spectral variability due to water make freshwater environments challenging to map using remote sensing technology. Satellite images available for New Zealand were reviewed, in terms of cost, and spectral and spatial resolution. Particularly promising image data sets for freshwater mapping include the QuickBird and SPOT-5. However, for mapping freshwater environments a combination of images is required to obtain high spatial, spectral, radiometric, and temporal resolution. Data fusion (DF) is a framework of data processing tools and algorithms that combines images to improve spectral and spatial qualities. A range of DF techniques were reviewed and tested for performance using panchromatic and multispectral QB images of a semi-aquatic environment, on the southern shores of Lake Taupo, New Zealand. In order to discuss the mechanics of different DF techniques a classification consisting of three groups was used - (i) spatially-centric (ii) spectrally-centric and (iii) hybrid. Subtract resolution merge (SRM) is a hybrid technique and this research demonstrated that for a semi aquatic QuickBird image it out performed Brovey transformation (BT), principal component substitution (PCS), local mean and variance matching (LMVM), and optimised high pass filter addition (OHPFA). However some limitations were identified with SRM, which included the requirement for predetermined band weights, and the over-representation of the spatial edges in the NIR bands due to their high spectral variance. This research developed three modifications to the SRM technique that addressed these limitations. These were tested on QuickBird (QB), SPOT-5, and Vexcel aerial digital images, as well as a scanned coloured aerial photograph. A visual qualitative assessment and a range of spectral and spatial quantitative metrics were used to evaluate these modifications. These included spectral correlation and root mean squared error (RMSE), Sobel filter based spatial edges RMSE, and unsupervised classification. The first modification addressed the issue of predetermined spectral weights and explored two alternative regression methods (Least Absolute Deviation, and Ordinary Least Squares) to derive image-specific band weights for use in SRM. Both methods were found equally effective; however, OLS was preferred as it was more efficient in processing band weights compared to LAD. The second modification used a pixel block averaging function on high resolution panchromatic images to derive spatial edges for data fusion. This eliminated the need for spectral band weights, minimised spectral infidelity, and enabled the fusion of multi-platform data. The third modification addressed the issue of over-represented spatial edges by introducing a sophisticated contrast and luminance index to develop a new normalising function. This improved the spatial representation of the NIR band, which is particularly important for mapping vegetation. A combination of the second and third modification of SRM was effective in simultaneously minimising the overall spectral infidelity and undesired spatial errors for the NIR band of the fused image. This new method has been labelled Contrast and Luminance Normalised (CLN) data fusion, and has been demonstrated to make a significant contribution in fusing multi-platform, multi-sensor, multi-resolution, and multi-temporal data. This contributes to improvements in the classification and monitoring of fresh water environments using remote sensing

Gradient variation: A key to enhancing photographs across illumination

Ph.DDOCTOR OF PHILOSOPH

Enhancing Low-Light Images Using Infrared-Encoded Images

Low-light image enhancement task is essential yet challenging as it is ill-posed intrinsically. Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss, which limits the capacity of recovering the brightness, contrast, and texture details due to the small number of income photons. In this work, we propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter, which allows for the capture of more photons and results in improved signal-to-noise ratio due to the inclusion of information from the IR spectrum. To verify the proposed strategy, we collect a paired dataset of low-light images captured without the IR cut-off filter, with corresponding long-exposure reference images with an external filter. The experimental results on the proposed dataset demonstrate the effectiveness of the proposed method, showing better performance quantitatively and qualitatively. The dataset and code are publicly available at https://wyf0912.github.io/ELIEI/Comment: The first two authors contribute equally. The work is accepted by ICIP 202