End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks

Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airport images using a limited amount of data. Second, to further improve the detection rate and reduce the false alarm rate, the concepts of “divide and conquer” and “integral loss’’ are introduced to establish cascade region proposal networks and multi-threshold detection networks, respectively. Third, hard example mining is used to improve the object discrimination ability and the training efficiency of the network during sample training. Additionally, a cross-optimization strategy is employed to achieve convolution layer sharing between the cascade region proposal networks and the subsequent multi-threshold detection networks, and this approach significantly decreases the detection time. The results show that the method established in this study can accurately detect various types of airports in complex backgrounds with a higher detection rate, lower false alarm rate, and shorter detection time than existing airport detection methods

GLOBAL BARE GROUND GAIN BETWEEN 2000 AND 2012 AND THE RELATIONSHIP WITH SOCIOECONOMIC DEVELOPMENT

Bare ground gain -- the complete removal of vegetation due to land use changes, represents an extreme land cover transition that completely alters the structure and functioning of ecosystems. The fast expansion of bare ground cover is directly associated with increasing population and urbanization, resulting in accelerated greenhouse gas emissions, intensified urban heat island phenomenon, and extensive habitat fragments and loss. While the economic return of settlement and infrastructure construction has improved human livelihoods, the negative impacts on the environment have disproportionally affected vulnerable population, creating inequality and tension in society. The area, distribution, drivers, and change rates of global bare ground gain were not systematically quantified; neither was the relationship between such dynamics and socioeconomic development. This dissertation seeks methods for operational characterization of bare ground expansion, advances our understanding of the magnitudes, dynamics, and drivers of global bare ground gain between 2000 and 2012, and uncovers the implications of such change for macro-economic development monitoring, all through Landsat satellite observations. The approach that employs wall-to-wall maps of bare ground gain classified from Landsat imagery for probability sample selection is proved particularly effective for unbiased area estimation of global, continental, and national bare ground gain, as a small land cover and land use change theme. Anthropogenic land uses accounted for 95% of the global bare ground gain, largely consisting of commercial/residential built-up, infrastructure development, and resource extraction. China and the United States topped the total area increase in bare ground. Annual change rates of anthropogenic bare ground gain are found as a leading indicator of macro-economic change in the study period dominated by the 2007-2008 global financial crisis, through econometric analysis between annual gains in the bare ground of different land use outcomes and economic fluctuations in business cycles measured by detrended economic variables. Instead of intensive manual interpretation of land-use attributes of probability sample, an approach of integrating a pixel- and an object- based deep learning algorithms is proposed and tested feasible for automatic attribution of airports, a transportation land use with economic importance

Mapping agricultural land in support of opium monitoring in Afghanistan with Convolutional Neural Networks (CNNs).

This work investigates the use of advanced image classification techniques for improving the accuracy and efficiency in determining agricultural areas from satellite images. The United Nations Office on Drugs and Crime (UNODC) need to accurately delineate the potential area under opium cultivation as part of their opium monitoring programme in Afghanistan. They currently use unsupervised image classification, but this is unable to separate some areas of agriculture from natural vegetation and requires time-consuming manual editing. This is a significant task as each image must be classified and interpreted separately. The aim of this research is to derive information about annual changes in land-use related to opium cultivation using convolutional neural networks with Earth observation data. Supervised machine learning techniques were investigated for agricultural land classification using training data from existing manual interpretations. Although pixel-based machine learning techniques achieved high overall classification accuracy (89%) they had difficulty separating between agriculture and natural vegetation at some locations. Convolutional Neural Networks (CNNs) have achieved ground-breaking performance in computer vision applications. They use localised image features and offer transfer learning to overcome the limitations of pixel-based methods. There are challenges related to training CNNs for land cover classification because of underlying radiometric and temporal variations in satellite image datasets. Optimisation of CNNs with a targeted sampling strategy focused on areas of known confusion (agricultural boundaries and natural vegetation). The results showed an improved overall classification accuracy of +6%. Localised differences in agricultural mapping were identified using a new tool called ‘localised intersection over union’. This provides greater insight than commonly used assessment techniques (overall accuracy and kappa statistic), that are not suitable for comparing smaller differences in mapping accuracy. A generalised fully convolutional model (FCN) was developed and evaluated using six years of data and transfer learning. Image datasets were standardised across image dates and different sensors (DMC, Landsat, and Sentinel-2), achieving high classification accuracy (up to 95%) with no additional training. Further fine-tuning with minimal training data and a targeted training strategy further increased model performance between years (up to +5%). The annual changes in agricultural area from 2010 to 2019 were mapped using the generalised FCN model in Helmand Province, Afghanistan. This provided new insight into the expansion of agriculture into marginal areas in response to counter-narcotic and alternative livelihoods policy. New areas of cultivation were found to contribute to the expansion of opium cultivation in Helmand Province. The approach demonstrates the use of FCNs for fully automated land cover classification. They are fast and efficient, can be used to classify satellite imagery from different sensors and can be continually refined using transfer learning. The proposed method overcomes the manual effort associated with mapping agricultural areas within the opium survey while improving accuracy. These findings have wider implications for improving land cover classification using legacy data on scalable cloud-based platforms.Simms, Daniel M. (Associate)PhD in Environment and Agrifoo

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing