Super-resolving multiresolution images with band-independant geometry of multispectral pixels

A new resolution enhancement method is presented for multispectral and multi-resolution images, such as these provided by the Sentinel-2 satellites. Starting from the highest resolution bands, band-dependent information (reflectance) is separated from information that is common to all bands (geometry of scene elements). This model is then applied to unmix low-resolution bands, preserving their reflectance, while propagating band-independent information to preserve the sub-pixel details. A reference implementation is provided, with an application example for super-resolving Sentinel-2 data.Comment: Source code with a ready-to-use script for super-resolving Sentinel-2 data is available at http://nicolas.brodu.net/recherche/superres

The Digital Earth Observation Librarian: A Data Mining Approach for Large Satellite Images Archives

Throughout the years, various Earth Observation (EO) satellites have generated huge amounts of data. The extraction of latent information in the data repositories is not a trivial task. New methodologies and tools, being capable of handling the size, complexity and variety of data, are required. Data scientists require support for the data manipulation, labeling and information extraction processes. This paper presents our Earth Observation Image Librarian (EOLib), a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data, in general. The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments (PGS). EOLib is composed of several modules which offer functionalities such as data ingestion, feature extraction from SAR (Synthetic Aperture Radar) data, meta-data extraction, semantic definition of the image content through machine learning and data mining methods, advanced querying of the image archives based on content, meta-data and semantic categories, as well as 3-D visualization of the processed images. EOLib is operated by DLR’s (German Aerospace Center’s) Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen, Germany

Impact of Feature Representation on Remote Sensing Image Retrieval

Remote sensing images are acquired using special platforms, sensors and are classified as aerial, multispectral and hyperspectral images. Multispectral and hyperspectral images are represented using large spectral vectors as compared to normal Red, Green, Blue (RGB) images. Hence, remote sensing image retrieval process from large archives is a challenging task.  Remote sensing image retrieval mainly consist of feature representation as first step and finding out similar images to a query image as second step. Feature representation plays important part in the performance of remote sensing image retrieval process. Research work focuses on impact of feature representation of remote sensing images on the performance of remote sensing image retrieval. This study shows that more discriminative features of remote sensing images are needed to improve performance of remote sensing image retrieval process

Monitoring and Detection of Hotspots using Satellite Images

Nowadays, the usage of optical remote sensing NOAA-AVHRR satellite data is getting familiar as it is known can save cost in order to capture a wide coverage of ground image. The captured images are meaningful after several processes done over it to produce hotspot detection. Developing a specific database to store information of Hotspots (LAC images) would make datamanagement and archiving purpose in more efficient and systematic way. Real-time data gathered are monitored countries such as Malaysia, Thailand, Singapore, Indonesia and Brunei within the region of NOAA Satellite coverage area. PostGIS, PostgreSQL, Mapserver and Autodesk MapGuide Studio software are to be studied as a guide to develop a system with simple database using object-relational database management system to store raster and vector images. This paper describes a solution for efficient handling of large raster image data sets in a standard object-relational database management system. By means of adequate indexing, retrieval techniques and multi resolution cell indexing (Quad-Tree) can be achieved using a standard DBMS, even for very large satellite images. Single image will be divided equally into 64 small squares (3 levels of image hierarchy - each level has 4 sub images of the higher image). Partial information of Daily Haze report (processed Hotspot on image map) produces by NREB can be viewed using web-based application. The final product of this project is a web-based application for displaying Hotspots on maps (combination of raster and vector images) with the ability to search record from database and functions to zoom in or zoom out the map. The objective of this paper is also to show the way satellite images and descriptive information are combined and amalgamated to form an Internet or Intranet application