Frugal Satellite Image Change Detection with Deep-Net Inversion

Change detection in satellite imagery seeks to find occurrences of targeted changes in a given scene taken at different instants. This task has several applications ranging from land-cover mapping, to anthropogenic activity monitory as well as climate change and natural hazard damage assessment. However, change detection is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we devise a novel algorithm for change detection based on active learning. The proposed method is based on a question and answer model that probes an oracle (user) about the relevance of changes only on a small set of critical images (referred to as virtual exemplars), and according to oracle's responses updates deep neural network (DNN) classifiers. The main contribution resides in a novel adversarial model that allows learning the most representative, diverse and uncertain virtual exemplars (as inverted preimages of the trained DNNs) that challenge (the most) the trained DNNs, and this leads to a better re-estimate of these networks in the subsequent iterations of active learning. Experiments show the out-performance of our proposed deep-net inversion against the related work.Comment: arXiv admin note: text overlap with arXiv:2212.1397

Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection

Satellite image change detection aims at finding occurrences of targeted changes in a given scene taken at different instants. This task is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we investigate satellite image change detection using active learning. Our method is interactive and relies on a question and answer model which asks the oracle (user) questions about the most informative display (dubbed as virtual exemplars), and according to the user's responses, updates change detections. The main contribution of our method consists in a novel adversarial model that allows frugally probing the oracle with only the most representative, diverse and uncertain virtual exemplars. The latter are learned to challenge the most the trained change decision criteria which ultimately leads to a better re-estimate of these criteria in the following iterations of active learning. Conducted experiments show the out-performance of our proposed adversarial display model against other display strategies as well as the related work.Comment: arXiv admin note: substantial text overlap with arXiv:2203.1155

Frugal Reinforcement-based Active Learning

Most of the existing learning models, particularly deep neural networks, are reliant on large datasets whose hand-labeling is expensive and time demanding. A current trend is to make the learning of these models frugal and less dependent on large collections of labeled data. Among the existing solutions, deep active learning is currently witnessing a major interest and its purpose is to train deep networks using as few labeled samples as possible. However, the success of active learning is highly dependent on how critical are these samples when training models. In this paper, we devise a novel active learning approach for label-efficient training. The proposed method is iterative and aims at minimizing a constrained objective function that mixes diversity, representativity and uncertainty criteria. The proposed approach is probabilistic and unifies all these criteria in a single objective function whose solution models the probability of relevance of samples (i.e., how critical) when learning a decision function. We also introduce a novel weighting mechanism based on reinforcement learning, which adaptively balances these criteria at each training iteration, using a particular stateless Q-learning model. Extensive experiments conducted on staple image classification data, including Object-DOTA, show the effectiveness of our proposed model w.r.t. several baselines including random, uncertainty and flat as well as other work.Comment: arXiv admin note: text overlap with arXiv:2203.1156

Ecology of Savanna Ecosystems in Indonesia

Tropical savannas in South East Asia are important yet understudied ecosystems. In fact, the description of savanna is limited in the Indonesian Archipelago, a region which, based on climate alone, would be expected to have mostly forest. In this thesis, I compared and contrasted the vegetation characteristics of four savannas in the wetter part of the Indonesian archipelago (Java – Bali – Lombok) to understand how fire and grazing influence their dynamics, and searched for evidence of savanna origins, maintenance, invasion by exotic/woody species and possible ecosystem transitions. Vegetation surveys, remote sensing techniques and statistical models were used to spatially and temporally analyse the savanna community composition and the environmental variables and disturbances that influence their structure. there are distinct elevation gradients (along with related climatic factors such as temperature and precipitation), as well as fire regime gradients, linked to tropical savanna community composition across Java, Bali and Lombok Islands. These compositions are characterized by different sets of species, and where invasive alien species are becoming significant components of the ecosystems. Lack of prescribed fire and a range of invasive species threaten to convert savanna at Bali Barat and Alas Purwo into secondary forests or shrubland, whereas the presence of forest pioneer/edge species within the savanna at Rinjani suggests successional change from grassland to forest may occur in the absence of future fires (although the role of soil, topography and microclimate in maintaining grass dominance needs also to be further explored). Compared to the others studied, the savanna in Baluran National Park has characteristics of being relatively old and persistent rather than one being created and maintained via recent human conversion of forests. Overall in Indonesia, there is much less savanna compared to forests, hence it is expected that a greater percentage of savanna is burned. Using remote sensing analysis, I confirmed that approximately 2% of savanna/open vegetation had burned over a 14 year period, whereas only 0.8% of forest has burned across Indonesia. The extent and frequency of burning is mostly associated with annual Southern Oscillation Index (SOI). Most burning occurred in years when the SOI sustained negative SOI values, which generally means drier conditions across South East Asia. I also developed species distribution models for the main invasive alien species of the savanna ecosystems studied, Acacia nilotica, to establish its invasion potential, both locally in Baluran National Park and regionally in other parts of Indonesia. Acacia nilotica was different from the other invasive species studied, in that it is promoted by herbivory, and possibly also by fire. It appears that spatially, A. nilotica is rapidly advancing into the savanna of Baluran National Parks where it was observed that over fourteen years the savanna size has decreased (-1,361 ha), whilst the A. nilotica stand has increased in area (+ 1,886 ha). It was demonstrated that fire and grazing play an important role in this invasion. Results also show that global climate change is likely to increase the potential distribution of A. nilotica in Indonesia and the area at risk of invasion. By year 2045, A. nilotica has potential to spread across much of the eastern parts of Indonesia. As fire and grazing are common to savannas of eastern Indonesia, they are likely to facilitate its invasion into these areas. In summary, I have shown that savanna plant community in Indonesia is formed and maintained by interactions between climatic factors, fire regime and grazing. Invasive species were also present in the studied savannas such as Chromolaena odorata, and Lantana camara. These invasive species together with forest pioneer/edge specialist species (Ficus septica, Laportea stimulans, Melastoma polyanthum, Nauclea orientalis, Rubus rosifolius), may also be increasing in absence of fire and also may be leading to change of state from savanna to dense woody vegetation. Absence of fire seems to be changing structure and floristic of savanna vegetation which has implications for savanna species including rare fauna such as Jalak Bali/Bali Starling (Leucopsar rotschildi) and Javan Banteng/Wild Java cattle (Bos javanicus subsp. javanicus). Results from this thesis showed that Bali Starling range in Bali Island has shrunk to remaining small patches of fire-induced open shrub and savanna woodland found below an elevation of 150–175 m in the north-east part of peninsular Prapat Agung of Bali Barat National Park. The description of the savanna dynamics presented here provides further evidence of the complexity of the savanna ecosystem and its susceptibility to change as a result of changing fire regimes and invasion by invasive species. A greater understanding of the possible ecosystem processes driving the dynamics of the savannas will assist in the formulation of successful savanna management strategies at local and regional scales

Automatic Flight Control Systems

The history of flight control is inseparably linked to the history of aviation itself. Since the early days, the concept of automatic flight control systems has evolved from mechanical control systems to highly advanced automatic fly-by-wire flight control systems which can be found nowadays in military jets and civil airliners. Even today, many research efforts are made for the further development of these flight control systems in various aspects. Recent new developments in this field focus on a wealth of different aspects. This book focuses on a selection of key research areas, such as inertial navigation, control of unmanned aircraft and helicopters, trajectory control of an unmanned space re-entry vehicle, aeroservoelastic control, adaptive flight control, and fault tolerant flight control. This book consists of two major sections. The first section focuses on a literature review and some recent theoretical developments in flight control systems. The second section discusses some concepts of adaptive and fault-tolerant flight control systems. Each technique discussed in this book is illustrated by a relevant example

Research & Technology Report Goddard Space Flight Center

The main theme of this edition of the annual Research and Technology Report is Mission Operations and Data Systems. Shifting from centralized to distributed mission operations, and from human interactive operations to highly automated operations is reported. The following aspects are addressed: Mission planning and operations; TDRSS, Positioning Systems, and orbit determination; hardware and software associated with Ground System and Networks; data processing and analysis; and World Wide Web. Flight projects are described along with the achievements in space sciences and earth sciences. Spacecraft subsystems, cryogenic developments, and new tools and capabilities are also discussed

Research and Technology Report. Goddard Space Flight Center

This issue of Goddard Space Flight Center's annual report highlights the importance of mission operations and data systems covering mission planning and operations; TDRSS, positioning systems, and orbit determination; ground system and networks, hardware and software; data processing and analysis; and World Wide Web use. The report also includes flight projects, space sciences, Earth system science, and engineering and materials

Data bases and data base systems related to NASA's Aerospace Program: A bibliography with indexes

This bibliography lists 641 reports, articles, and other documents introduced into the NASA scientific and technical information system during the period January 1, 1981 through June 30, 1982. The directory was compiled to assist in the location of numerical and factual data bases and data base handling and management systems

NASA Tech Briefs, May 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences