Improved POLSAR Image Classification by the Use of Multi-Feature Combination

Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data. Both polarimetric decomposition and time-frequency (TF) decomposition were used to mine the hidden information of objects in POLSAR data, which was then applied in the C5.0 decision tree algorithm for optimal feature selection and classification. Using a NASA/JPL AIRSAR POLSAR scene as an example, the overall accuracy and kappa coefficient of the proposed method reached 91.17% and 0.90 in the L-band, much higher than those achieved by the commonly applied Wishart supervised classification that were 45.65% and 0.41. Meantime, the overall accuracy of the proposed method performed well in both C- and P-bands. Polarimetric decomposition and TF decomposition all proved useful in the process. TF information played a great role in delineation between urban/built-up areas and vegetation. Three polarimetric features (entropy, Shannon entropy, T11 Coherency Matrix element) and one TF feature (HH intensity of coherence) were found most helpful in urban areas classification. This study indicates that the integrated use of polarimetric decomposition and TF decomposition of POLSAR data may provide improved feature extraction in heterogeneous urban areas

A Machine Learning Framework for the Classification of Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface Reflectance Data

Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types

Predicting plant environmental exposure using remote sensing

Wheat is one of the most important crops globally with 776.4 million tonnes produced in 2019 alone. However, 10% of all wheat yield is predicted to be lost to Septoria Tritici Blotch (STB) caused by Zymoseptoria tritici (Z. tritici). Throughout Europe farmers spend £0.9 billion annually on preventative fungicide regimes to protect wheat against Z. tritici. A preventative fungicide regime is used as Z. tritici has a 9-16 day asymptomatic latent phase which makes it difficult to detect before symptoms develop, after which point fungicide intervention is ineffective. In the second chapter of my thesis I use hyperspectral sensing and imaging techniques, analysed with machine learning to detect and predict symptomatic Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of symptomatic Z. tritici infection and could facilitate precision agriculture methods, to use in the subsequent growing season, that optimise fungicide use and increase yield. In the third chapter of my thesis, I develop a multispectral imaging system which can detect and utilise none visible shifts in plant leaf reflectance to distinguish plants based on the nitrogen source applied. Currently, plants are treated with nitrogen sources to increase growth and yield, the most common being calcium ammonium nitrate. However, some nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive manufacture and ammonium sulphate in the cultivation and extraction of the narcotic cocaine from Erythroxylum spp. In my third chapter I show that hyperspectral sensing, multispectral imaging, and machine learning image analysis can be used to visualise and differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis of leaves from plants exposed to different nitrogen sources reveals shifts in colourful metabolites that may contribute to altered reflectance signatures. This suggests that different nitrogen feeding regimes alter plant secondary metabolism leading to changes in plant leaf reflectance detectable via machine learning of multispectral data but not the naked eye. These results could facilitate the development of technologies to monitor illegal activities involving various nitrogen sources and further inform nitrogen application requirements in agriculture. In my fourth chapter I implement and adapt the hyperspectral sensing, multispectral imaging and machine learning image analysis developed in the third chapter to detect asymptomatic (and symptomatic) Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of all stages of Z. tritici infection and could facilitate precision agriculture methods to be used during the current growing season that optimise fungicide use and increase yield.Open Acces

Review of Classification Algorithms with Changing Inter-Class Distances

Peer reviewedPublisher PD

LiDAR-Assisted Extraction of Old Growth Baldcypress Stands Along The Black River of North Carolina

The remnants of ancient baldcypress forests continue to grow across the Southeastern United States. These long lived trees are invaluable for biodiversity along riverine ecosystems, provide habitat to a myriad of animal species, and augment the proxy climate record for North America. While extensive logging of the areas along the Black River in North Carolina has mostly decimated ancient forests of many species including the baldcypress, conservation efforts from The Nature Conservancy and other partners are under way. In order to more efficiently find and study these enduring stands of baldcypress, some of which are estimated to be more than 1,000 years old, LiDAR remote sensing and geospatial analysis techniques can be employed. Promising results have been discovered correlating LiDAR-derived metrics and known stands of old growth baldcypress. A number of percentile height metrics and other composite metrics like canopy cover and density were extracted from LiDAR data collected across North Carolina. Along with the metrics, locations of known stands of old growth were used as training data for a supervised classification with the C5.0 decision tree algorithm. C5.0 was used to condense the patterns found across the training data into a set of rules that could then be applied to other areas within the study site or anywhere else across the LiDAR data. Both existing stands and new areas were selected by the machine learning rulesets indicating that the use of machine learning is valid to identify stands of ancient trees along the Black River. Overall C5.0 accuracies of approximately 98.5% (based on training data) and 88.6% (based on independent test data) were achieved. More than 8 km2 of predicted old growth forests, outside of available in situ reference areas, were also identified within the Black River site

Hybrid Image Classification Technique for Land-Cover Mapping in the Arctic Tundra, North Slope, Alaska

Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was used as an input to a Multilayer Perceptron (MLP) classifier. The result from the MLP classifier was compared to the previous classified map and for the pixels where there was a disagreement for the class allocations, the class having a higher kappa value was assigned to the pixel in the final classified map. The results were compared to standard classification techniques: simple unsupervised clustering technique and supervised classification with Feature Analyst. The results indicated higher classification accuracy (75.6%, with kappa value of .6840) for the proposed hybrid classification method than the standard classification techniques: unsupervised clustering technique (68.3%, with kappa value of 0.5904) and supervised classification with Feature Analyst (62.44%, with kappa value of 0.5418). The results were statistically significant at 95% confidence level