Abstracting GIS Layers from Hyperspectral Imagery

The spectral-spatial relationship of materials in a hyperspectral image cube is exploited to partially automate the creation of Geographic Information System (GIS) layers. The topological neighborhood preservation property of the Self Organizing Map (SOM) is clustered into six (partially overlapping) neighborhoods that are mapped into the image domain to locate in-scene structures of similar material type. GIS layers are abstracted through spatial logical and morphological operations on the six image domain material maps and a novel road finding algorithm connects road segments under significant tree-occlusion resulting in a contiguous road network. It is assumed that specific knowledge of the scene (e.g. endmember spectra) is not available. The results are eight separate high-quality GIS layers (Vegetation, Trees, Fields, Buildings, Major Buildings, Roadways, and Parking Areas) that follow the scene features of the hyperspectral image and are separately and automatically labeled. The material maps resulting from clustering the SOM have an 84.3% average accuracy, which increases to 93.9% after spatial processing into GIS layers

Abstracting GIS Layers from Hyperspectral Imagery

Modern warfare methods in the urban environment necessitates the use of multiple layers of sensors to manage the battle space. Hyperspectral imagers are one possible sensor modality to provide remotely sensed images that can be converted into Geographic Information Systems (GIS) layers. GIS layers abstract knowledge of roads, buildings, and scene content and contain shape files that outline and highlight scene features. Creating shape files is a labor-intensive and time-consuming process. The availability of shape files that reflect the current configuration of an area of interest significantly enhances Intelligence Preparation of the Battlespace (IPB). The solution presented in this thesis is a novel process to automate the creation of shape files by exploiting the spectral-spatial relationship of a hyperspectral image cube. It is assumed that “a-priori” endmember spectra, a spectral database, or specific scene knowledge is not available. The topological neighborhood of a Self Organizing Map (SOM) is segmented and used as a spectral filter to produce six initial object maps that are spatially processed with logical and morphological operations. A novel road finding algorithm connects road segments under significantly tree-occluded roadways into a contiguous road network. The manual abstraction of GIS shape files is improved into a semi-automated process. The resulting shape files are not susceptible to deviation from orthorectified imagery as they are produced directly from the hyperspectral imagery. The results are eight separate high-quality GIS layers (Vegetation, Non-Tree Vegetation, Trees, Fields, Buildings, Major Buildings, Roadways, and Parking Areas) that follow the terrain of the hyperspectral image and are separately and automatically labeled. Spatial processing improves layer accuracy from 85% to 94%. Significant layer accuracies include the “road network” at 93%, “buildings” at 97%, and “major buildings” at 98%

Context Aided Tracking with Adaptive Hyperspectral Imagery

A methodology for the context-aided tracking of ground vehicles in remote airborne imagery is developed in which a background model is inferred from hyperspectral imagery. The materials comprising the background of a scene are remotely identified and lead to this model. Two model formation processes are developed: a manual method, and method that exploits an emerging adaptive, multiple-object-spectrometer instrument. A semi-automated background modeling approach is shown to arrive at a reasonable background model with minimal operator intervention. A novel, adaptive, and autonomous approach uses a new type of adaptive hyperspectral sensor, and converges to a 66% correct background model in 5% the time of the baseline {a 95% reduction in sensor acquisition time. A multiple-hypothesis-tracker is incorporated, which utilizes background statistics to form track costs and associated track maintenance thresholds. The context-aided system is demonstrated in a high- fidelity tracking testbed, and reduces track identity error by 30%

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.Comment: This manuscript is the accepted version for Proceedings of the IEE

Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes

Soil salinization is one of the severe land-degradation problems due to its adverse effects on land productivity. Each year several hectares of lands are degraded due to primary or secondary soil salinization, and as a result, it is becoming a major economic and environmental concern in different countries. Spatio-temporal mapping of soil salinity is therefore important to support decisionmaking procedures for lessening adverse effects of land degradation due to the salinization. In that sense, satellite-based technologies provide cost effective, fast, qualitative and quantitative spatial information on saline soils. The main objective of this work is to highlight the recent remote sensing (RS) data and methods to assess soil salinity that is a worldwide problem. In addition, this study indicates potential linkages between salt-affected land and the prevailing climatic conditions of the case study areas being examined. Web of science engine is used for selecting relevant articles. "Soil salinity" is used as the main keyword for finding "articles" that are published from January 1, 2007 up to April 30, 2018. Then, 3 keywords; "remote sensing", "satellite" and "aerial" were used to filter the articles. After that, 100 case studies from 27 different countries were selected. Remote sensing based researches were further overviewed regarding to their location, spatial extent, climate regime, remotely sensed data type, mapping methods, sensing approaches together with the reason of salinity for each case study. In addition, soil salinity mapping methods were examined to present the development of different RS based methods with time. Studies are shown on the Köppen-Geiger climate classification map. Analysis of the map illustrates that 63% of the selected case study areas belong to arid and semi-arid regions. This finding corresponds to soil characteristics of arid regions that are more susceptible to salinization due to extreme temperature, high evaporation rates and low precipitation

TerraSenseTK: a toolkit for remote soil nutrient estimation

Intensive farming endangers soil quality in various ways. Researchers show that if these practices continue, humanity will be faced with food production issues. For this matter, Earth Observation, more concretely Soil Sensing, along with Machine Learning, can be employed to monitor several indicators of soil degradation, such as soil salinity, soil heavy metal contamination and soil nutrients estimation. More concretely, Soil Nutrients are of great importance. For instance, to understand which crop better suits the land, the soil nutrients must be identified. However, sampling soil is a laborous and expensive task, which can be leveraged by Remote Sensing and Machine Learning. Several studies have already been developed in this matter, although many gaps still exist. Among them, the lack of cross-dataset evaluations of existing algorithms, and also the steep learning curve to the Earth Observation domain that prevents many researchers from embracing this field. In this sense, we propose TerraSense ToolKit (TSTK), a python toolkit that addresses these challenges. In this work, the possibility to use Remote sensing along with Machine Learning algorithms to per form Soil Nutrient Estimation is explored, additionally, a nutrient estimation toolkit is proposed, and the effectivity of it is tested in a soil nutrient estimation case study. This toolkit is capable of simplifying Remote Sensing experiments and aims at reducing the barrier to entry to the field of Earth Observation. It comes with a preconfigured case study which implements a soil sensing pipeline. To evaluate the usability of the toolkit, experiments with five different crops were executed, namely with Wheat, Barley, Maize, Sunflower and Vineyards. This case study gave visibility to an underlying unbalanced data problem, which is not well addressed in the current State of the Art.A agricultura intensiva poe em perigo a qualidade do solo de v ˜ arias formas. Os investigadores ´ mostram que, se continuarmos com estas praticas, a humanidade ser ´ a confrontada com quest ´ oes de ˜ produc¸ao alimentar. Para este efeito, a Observac¸ ˜ ao da Terra, mais concretamente o Sensoriamento ˜ do Solo, juntamente com a aprendizagem automatica, podem ser utilizadas para monitorizar v ´ arios ´ indicadores da degradac¸ao do solo, tais como a salinidade do solo, a contaminac¸ ˜ ao do solo por metais ˜ pesados e a quantificac¸ao dos nutrientes do solo. Mais concretamente, os Nutrientes do Solo s ˜ ao de ˜ grande importancia. Por exemplo para compreender qual a cultura que melhor se adapta ao solo, os ˆ nutrientes do solo devem ser identificados. No entanto, a amostragem do solo e uma tarefa trabalhosa ´ e dispendiosa, que pode ser impulsionada pela percepc¸ao remota e pela aprendizagem autom ˜ atica. ´ Ja foram desenvolvidos v ´ arios estudos sobre este assunto, embora ainda existam muitas lacunas. ´ Entre eles, a falta de avaliac¸oes cruzadas dos algoritmos existentes, e tamb ˜ em a curva de aprendiza- ´ gem acentuada para o campo de Observac¸ao da Terra que impede muitos investigadores de enveredar ˜ por este campo. Neste sentido, propomos TSTK, um toolkit em python que aborda estes desafios. Neste trabalho, e explorada a possibilidade de usar a Percepc¸ ´ ao Remota juntamente com os algo- ˜ ritmos de Aprendizagem Automatica para realizar a Estimativa de Nutrientes do Solo. Al ´ em disso, ´ e´ proposto um toolkit de estimativa de nutrientes e tambem um pipeline para o devido efeito, a efetividade ´ do toolkit e testada num caso de estudo de Estimac¸ ´ ao de Nutrientes no Solo. ˜ Este toolkit e capaz de simplificar as experi ´ encias de Percepc¸ ˆ ao Remota e visa reduzir a barreira ˜ de entrada no campo da Observac¸ao da Terra. Para avaliar a usabilidade do toolkit, foram executadas ˜ experiencias com cinco culturas diferentes, nomeadamente Trigo, Cevada, Milho, Girassol e Vinha. Este ˆ caso de estudo deu visibilidade a um problema subjacente de dados desiquilibrados, o qual nao˜ e bem ´ identificado no Estado da Arte atual

Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture

L'abstract è presente nell'allegato / the abstract is in the attachmen