Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

Gabor Filter and Rough Clustering Based Edge Detection

This paper introduces an efficient edge detection method based on Gabor filter and rough clustering. The input image is smoothed by Gabor function, and the concept of rough clustering is used to focus on edge detection with soft computational approach. Hysteresis thresholding is used to get the actual output, i.e. edges of the input image. To show the effectiveness, the proposed technique is compared with some other edge detection methods.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201

Sonora exploratory study for the detection of wheat-leaf rust

The applicability of LANDSAT remote sensing technology to the detection of a wheat-leaf-rust epidemic in Sonora, Mexico, during 1977 was investigated. LANDSAT data acquired during crop years 1975-76 and 1976-77 were clustered, classified, and analyzed in order to detect agricultural changes. Analysis of 1977 data indicates a significant proportion of the identified wheat is stressed (potentially rust-infected). Additional analyses show a significant increase in fallowing during the year, as well as a substantial decrease in reservoir levels in the Sonora agricultural region. Ground observations are required to substantiate these analyses. The possibility exists that heat-rust is not LANDSAT detectable and that the clusters identified as containing stressed signatures represent different varieties of wheat or perhaps nonwheat crops

Patterns and Trends in Chlorophyll-a Concentration and Phytoplankton Phenology in the Biogeographical Regions of Southwestern Atlantic

The Southwestern Atlantic Ocean (SWA), is considered one of the most productive areas of the world, with a high abundance of ecologically and economically important fish species. Yet, the biological responses of this complex region to climate variability are still uncertain. Here, using 24 years of satellite-derived Chl-a data, we classified the SWA into 9 spatially coherent regions based on the temporal variability of Chl-a concentration, as revealed by SOM (Self-Organizing Maps) analysis. These biogeographical regions were the basis of a regional trend analysis in phytoplankton biomass, phenological indices, and environmental forcing variations. A general positive trend in phytoplankton concentration was observed, especially in the highly productive areas of the northern shelf-break, where phytoplankton biomass has increased at a rate of up to 0.42 ± 0.04 mg m−3 per decade. Significant positive trends in sea surface temperature were observed in 4 of the 9 regions (0.08–0.26 °C decade−1) and shoaling of the mixing layer depth in 5 of the 9 regions (−1.50 to −3.36 m decade−1). In addition to the generally positive trend in Chl-a, the most conspicuous change in the phytoplankton temporal patterns in the SWA is a delay in the autumn bloom (between 15 ± 3 and 24 ± 6 days decade−1, depending on the region). The observed variations in phytoplankton phenology could be attributed to climate-induced ocean warming and extended stratification period. Our results provided further evidence of the impact of climate change on these highly productive waters.Fil: Delgado, Ana Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Hernández Carrasco, Ismael. Consejo Superior de Investigaciones Científicas. Instituto Mediterráneo de Estudios Avanzados; EspañaFil: Combes, Vincent. Consejo Superior de Investigaciones Científicas. Instituto Mediterráneo de Estudios Avanzados; España. Universitat de Les Illes Balears; EspañaFil: Font Muñoz, Joan. Consejo Superior de Investigaciones Científicas. Instituto Mediterráneo de Estudios Avanzados; EspañaFil: Pratolongo, Paula Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Basterretxea, Gotzon. Consejo Superior de Investigaciones Científicas. Instituto Mediterráneo de Estudios Avanzados; Españ

Land Use/Land Cover Change Detection by Multi-Temporal Remote Sensing Imageries: Bangalore City India (1992-2012)

Land use and land cover (LULC) changes is a dynamic, widespread and accelerating process, mainly driven by natural phenomena and anthropogenic activities, which in turn drives changes that world impact natural ecosystem. Change detection is one of the landscape ecological aims. Main aim of this study is to prepare land use land cover and their change detections by using remote sensing and GIS techniques. This paper presents the land use/land cover changes that have taken place in Bangalore, from 1992 to 2012.The study has been done through Landsat & IRS imagery from 1992, 2000, 2004, 2005, 2006, 2009 and 2012. The land use and land cover classification maps were prepared through remote sensing and GIS technology. The results indicate that there was a significant increasing trend in built up land and decreasing trend in agricultural lan

Unsupervised Burned Area Estimation through Satellite Tiles: A multimodal approach by means of image segmentation over remote sensing imagery

Climate change is increasing the number and the magnitude of wildfires, which become every year more severe. An accurate delineation of burned areas, which is often done through time consuming and inaccurate manual approaches, is of paramount importance to estimate the economic impact of such events. In this paper we introduce Burned Area Estimation through satellite tiles (BAE), an unsupervised algorithm that couples image processing techniques and an unsupervised neural network to automatically delineate the burned areas of wildfires from satellite imagery. We show its capabilities by performing an evaluation over past wildfires across European and non-European countries

Deep Learning for Land Cover Change Detection

Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. According to the provided metrics, both multitemporal LSTM approaches outperform the monotemporal FCN approach, about 3 to 5 percentage points (p.p.). The LSTM approach trained on the variable sequences detects 3 p.p. more land cover changes than the LSTM approach trained on the fixed sequences. Besides, applying our selected pre-processing improves the water classification and avoids reducing the dataset effectively by 17.6%. The presented LSTM approaches can be modified to provide applicability for a variable number of image sequences since we published the code of the deep learning models. The Sentinel-2 data and the ground truth are also freely available

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

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%