Robust hyperspectral image classification with rejection fields

In this paper we present a novel method for robust hyperspectral image classification using context and rejection. Hyperspectral image classification is generally an ill-posed image problem where pixels may belong to unknown classes, and obtaining representative and complete training sets is costly. Furthermore, the need for high classification accuracies is frequently greater than the need to classify the entire image. We approach this problem with a robust classification method that combines classification with context with classification with rejection. A rejection field that will guide the rejection is derived from the classification with contextual information obtained by using the SegSALSA algorithm. We validate our method in real hyperspectral data and show that the performance gains obtained from the rejection fields are equivalent to an increase the dimension of the training sets.Comment: This paper was submitted to IEEE WHISPERS 2015: 7th Workshop on Hyperspectral Image and Signal Processing: Evolution on Remote Sensing. 5 pages, 1 figure, 2 table

Estimate Broad of Natural Mineral Resources Area Lateritic Nickel Based of Image Analysis Satellite Landsat 7 Etm+ In District Laonti, Konawe Selatan, Province of Southeast Sulawesi

Mineral exploration is one of the important activities to obtain location information about where the minerals are, but this exploration process takes years and costly especially when carried out over a wide area. Therefore through this study the application of Geographic Information Systems and Remote Sensing for mapping the distribution of potential mineral deposits of lateritic nickel (Ni) is tested. The method used is the analysis of digital data Landsat 7 ETM +. Rocessed image data by performing a technique sharpening contrast, filtering, creation of a composite image and image fusion. Image data processing is for the interpretation of visual straightness, limit unit morphology and the estimation of mineral lateritic nickel. The data as well as analysis techniques NDVI in order to obtain the pattern of vegetation density on the surface. The results showed that the presence of lateritic nickel mineral formed on ultramafic rocks undergoing the process of weathering and serpentinization. Characterized with the appearance of geological structures identified as robust and fault structures. Which are also represent on the remote sensing images as rectangular flow patterns.Ultramafic rocks are located in the morphological undulating hills. Vegetation is identified growing on ultramafic rocks are categorized as dense vegetation. Vegetation that grows in the form of a single tree with an average diameter ≤30 cm. result generating estimates of mineral potential areas of lateritic nickel has an area ranging 6.3 ha

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Ocean Eddy Identification and Tracking using Neural Networks

Global climate change plays an essential role in our daily life. Mesoscale ocean eddies have a significant impact on global warming, since they affect the ocean dynamics, the energy as well as the mass transports of ocean circulation. From satellite altimetry we can derive high-resolution, global maps containing ocean signals with dominating coherent eddy structures. The aim of this study is the development and evaluation of a deep-learning based approach for the analysis of eddies. In detail, we develop an eddy identification and tracking framework with two different approaches that are mainly based on feature learning with convolutional neural networks. Furthermore, state-of-the-art image processing tools and object tracking methods are used to support the eddy tracking. In contrast to previous methods, our framework is able to learn a representation of the data in which eddies can be detected and tracked in more objective and robust way. We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.Comment: accepted for International Geoscience and Remote Sensing Symposium 201

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