Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (Epics) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to help the understanding of the Earth’s complex systems and to monitor significant changes to them. The quality of data recorded by these sensors is a primary concern, as it critically depends on accurate radiometric calibration for each sensor. Pseudo Invariant Calibration Sites (PICS) have been extensively used for radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. In addition, this work proposes a technique to generate a representative hyperspectral profile for these clusters, as the hyperspectral profile of these identified clusters are mandatory in order to utilize them for performing cross-calibration of optical satellite sensors. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. Furthermore, this work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor crosscalibration. Eventually, North Africa can be used a continental scale PICS

A Practical Study on Recovering Spectra from RGB Images

RGB cameras make three measurements of the light entering the camera, whereas hyperspectral imaging devices, per pixel, record the spectrum of the light. Spectral images have been shown to be more useful than RGB images in solving problems in many industrial application areas, including remote sensing and medical imaging. Spectral Reconstruction (SR) refers to a computational algorithm that recovers spectra from the RGB camera responses. This “make-the-RGBs-more-informative” process is most commonly implemented by machine learning (ML) algorithms, given matching RGB and hyperspectral data for training. Two mainstream ML approaches used in SR are regression and Deep Neural Network (DNN). While the former often has simple closed-form formulations for a pixel-based mapping, the latter approach is much more complicated: millions of parameters are used to map large image patches, in the hope that the network could utilise the spatial context in which each RGB is seen to further improve SR. It is generally accepted that regressions have long since been superseded by DNN methods. Nevertheless, few studies have actually been dedicated to comparing the two approaches. There are three main goals of this thesis. First, we benchmark regression- and DNN-based SR algorithms on the same hyperspectral image dataset. Here we pay close attention to the role that the spectral sensitivities of a camera play and also SR performance on unseen data. Second, we seek to improve regression-based algorithms and, in effect, attempt to close their gap in performance compared to DNN counterparts. Lastly, we investigate the practical issues faced by all SR algorithms. We consider SR performance as exposure changes and SR performance in a “closed-loop” imaging framework (i.e., do the spectra that an SR algorithm recovers integrate to the same input RGBs?). Our baseline benchmarking experiments indicate that the best DNN method only delivers a 12% accuracy improvement compared to the best-performing regression. Moreover, a regression method trained for one camera might actually outperform a DNN trained on another camera. Additionally, we find that the DNN’s worst-case performance (for unseen and unexpected scenes) is no better than the simplest regression method. Concomitantly, this encourages us to see if we could improve the average performance of regression methods. We propose three new improvements for regression methods. First, we reformulate the regressions so that they minimise a loss metric that is more similar to the one used to rank and train the leading DNN methods. Secondly, we revisit the regularisation step of the regression implementation. Regularisation is a technique for making the outputs of regressions more stable for unseen input and is usually governed by a single regularisation parameter. Here, we adopt as many regularisations as there are channels in a hyperspectral image, and this results in significant performance improvement. Lastly, we propose a new sparse regression framework. In sparse regression, we code RGBs in terms of the neighbourhood in the RGB space (via a clustering argument). We argue that this clustering is better performed in the spectral domain (where input RGBs are first regressed to some primary estimation of spectra). Combined, upgraded formulation and improved clustering, we develop a regression-based method found to work as well as the top DNN methods. As important as spectral accuracy is, trained SR algorithms need to work in practice, e.g., where objects and scenes can be viewed in varying exposure conditions. Unfortunately, we find that leading methods, such as non-linear regressions and DNNs, do not work well when exposure changes. Consequently, we propose new training frameworks which ensure the DNNs and regressions continue to work well under changing exposures. Finally, we investigate the following problem: we find that both regression- and DNNbased SR algorithms recover spectra that—when integrated with the camera’s spectral sensitivities—do not induce the same RGBs as the input to the algorithm. This means that the spectra that are recovered cannot (ever) be the correct spectra. Given this finding, we seek ways of adding physical plausibility (spectra should integrate to predict the input RGBs) to the SR algorithms. One of our proposed solutions is effectively a simple post-processing step which, provably, always improves the RMS (i.e., root-mean-square) performance of any SR algorithm

The effect of molecular, morphological and spectral reflectance evolution on nicotiana polyploids of different ages

PhDInterspecific hybridisation accompanied by multiplication of chromosome number, or allopolyploidy, results in rapid genetic, epigenetic, chromosomal and morphological changes. Evidence of ancient polyploidisation is found in most if not all angiosperms, suggesting that polyploidisation may have played a role in the success of angiosperm species. Here, I examine the fate of duplicate gene copies in the floral development gene NICOTIANA FLO/LFY (NFL) and the evolution of floral form and colour in Nicotiana (Solanaceae) allotetraploids of different ages. Both NFL copies are retained in all allotetraploids examined, even those ~10 million years old (myo). There are no nonsense or frame-shift mutations, suggesting that all copies are still functional. Both copies are expressed in all allotetraploids examined, even those ~4.5 myo. The evolution of floral form and colour was examined using geometric morphometrics of floral limb shape, corolla tube length and width metrics, and spectral reflectance measurements of floral colour. In floral limb shape, younger polyploids tend to be intermediate in shape between those of their diploid progenitors, whereas older polyploids have more divergent forms; however, divergence in floral limb shape can occur rapidly following polyploidisation. In corolla tube length and width, the majority of polyploids have wider and shorter corolla tubes, suggesting more generalist pollination after polyploidisation. In floral colour, polyploids can either be intermediate between their progenitors, like one or other progenitor, or divergent. The floral colour of N. tabacum is divergent and seems to have resulted from the inheritance of floral plastids that lack chlorophyll from its maternal progenitor and the inheritance of anthocyanin pigmentation from its paternal progenitor. Evidence for convergent evolution of floral form in green/yellow-flowered Nicotiana seems to be linked to hummingbird pollination. Overall, rapid molecular and morphological changes following polyploidisation may be advantageous and may partially explain why polyploids have been so successful in angiosperms

Tree species classification from AVIRIS-NG hyperspectral imagery using convolutional neural networks

This study focuses on the automatic classification of tree species using a three-dimensional convolutional neural network (CNN) based on field-sampled ground reference data, a LiDAR point cloud and AVIRIS-NG airborne hyperspectral remote sensing imagery with 2 m spatial resolution acquired on 14 June 2021. I created a tree species map for my 10.4 km2 study area which is located in the Jurapark Aargau, a Swiss regional park of national interest. I collected ground reference data for six major tree species present in the study area (Quercus robur, Fagus sylvatica, Fraxinus excelsior, Pinus sylvestris, Tilia platyphyllos, total n = 331). To match the sampled ground reference to the AVIRIS-NG 425 band hyperspectral imagery, I delineated individual tree crowns (ITCs) from a canopy height model (CHM) based on LiDAR point cloud data. After matching the ground reference data to the hyperspectral imagery, I split the extracted image patches to training, validation, and testing subsets. The amount of training, validation and testing data was increased by applying image augmentation through rotating, flipping, and changing the brightness of the original input data. The classifier is a CNN trained on the first 32 principal components (PC’s) extracted from AVIRIS-NG data. The CNN uses image patches of 5 × 5 pixels and consists of two convolutional layers and two fully connected layers. The latter of which is responsible for the final classification using the softmax activation function. The results show that the CNN classifier outperforms comparable conventional classification methods. The CNN model is able to predict the correct tree species with an overall accuracy of 70% and an average F1-score of 0.67. A random forest classifier reached an overall accuracy of 67% and an average F1-score of 0.61 while a support-vector machine classified the tree species with an overall accuracy of 66% and an average F1-score of 0.62. This work highlights that CNNs based on imaging spectroscopy data can produce highly accurate high resolution tree species distribution maps based on a relatively small set of training data thanks to the high dimensionality of hyperspectral images and the ability of CNNs to utilize spatial and spectral features of the data. These maps provide valuable input for modelling the distributions of other plant and animal species and ecosystem services. In addition, this work illustrates the importance of direct collaboration with environmental practitioners to ensure user needs are met. This aspect will be evaluated further in future work by assessing how these products are used by environmental practitioners and as input for modelling purposes

Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop

The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase

Snow and Ice Applications of AVHRR in Polar Regions: Report of a Workshop

The third symposium on Remote Sensing of Snow and Ice, organized by the International Glaciological Society, took place in Boulder, Colorado, 17-22 May 1992. As part of this meeting a total of 21 papers was presented on snow and ice applications of Advanced Very High Resolution Radiometer (AVHRR) satellite data in polar regions. Also during this meeting a NASA sponsored Workshop was held to review the status of polar surface measurements from AVHRR. In the following we have summarized the ideas and recommendations from the workshop, and the conclusions of relevant papers given during the regular symposium sessions. The seven topics discussed include cloud masking, ice surface temperature, narrow-band albedo, ice concentration, lead statistics, sea-ice motion and ice-sheet studies with specifics on applications, algorithms and accuracy, following recommendations for future improvements. In general, we can affirm the strong potential of AVHRR for studying sea ice and snow covered surfaces, and we highly recommend this satellite data set for long-term monitoring of polar process studies. However, progress is needed to reduce the uncertainty of the retrieved parameters for all of the above mentioned topics to make this data set useful for direct climate applications such as heat balance studies and others. Further, the acquisition and processing of polar AVHRR data must become better coordinated between receiving stations, data centers and funding agencies to guarantee a long-term commitment to the collection and distribution of high quality data

Uncertainty in Hyperspectral Remote Sensing: Analysis of the Potential and Limitation of Shallow Water Bathymetry and Benthic Classification

Propagating the inherent uncertainty in hyperspectral remote sensing is key in understanding the limitation and potential of derived bathymetry and benthic classification. Using an improved optimisation algorithm, the potential of detecting temporal bathymetric changes above uncertainty was quantified from a time series of hyperspectral imagery. A new processing approach was also developed that assessed the limitations and potential of benthic classification by analysing optical separability of substrates above total system uncertainty and attenuating water column