Use of Self Organized Maps for Feature Extraction of Hyperspectral Data

In this paper, the problem of analyzing hyperspectral data is presented. The complexity of multi-dimensional data leads to the need for computer assisted data compression and labeling of important features. A brief overview of Self-Organizing Maps and their variants is given and then two possible methods of data analysis are examined. These methods are incorporated into a program derived from som_toolbox2. In this program, ASD data (data collected by an Analytical Spectral Device sensor) is read into a variable, relevant bands for discrimination between classes are extracted, and several different methods of analyzing the results are employed. A GUI was developed for easy implementation of these three stages

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%

Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data

Traditional field-based lithological mapping can be a time-consuming, costly and challenging endeavour when large areas need to be investigated, where terrain is remote and difficult to access and where the geology is highly variable over short distances. Consequently, rock units are often mapped at coarse-scales, resulting in lithological maps that have generalised contacts which in many cases are inaccurately located. Remote sensing data, such as aerial photographs and satellite imagery are commonly incorporated into geological mapping programmes to obtain geological information that is best revealed by overhead perspectives. However, spatial and spectral limitations of the imagery and dense vegetation cover can limit the utility of traditional remote sensing products. The advent of Airborne Light Detection And Ranging (LiDAR) as a remote sensing tool offers the potential to provide a novel solution to these problems because accurate and high-resolution topographic data can be acquired in either forested or non-forested terrain, allowing discrimination of individual rock types that typically have distinct topographic characteristics. This study assesses the efficacy of airborne LiDAR as a tool for detailed lithological mapping in the upper section of the Troodos ophiolite, Cyprus. Morphometric variables (including slope, curvature and surface roughness) were derived from a 4 m digital terrain model in order to quantify the topographic characteristics of four principal lithologies found in the area. An artificial neural network (the Kohonen Self-Organizing Map) was then employed to classify the lithological units based upon these variables. The algorithm presented here was used to generate a detailed lithological map which defines lithological contacts much more accurately than the best existing geological map. In addition, a separate map of classification uncertainty highlights potential follow-up targets for ground-based verification. The results of this study demonstrate the significant potential of airborne LiDAR for lithological discrimination and rapid generation of detailed lithological maps, as a contribution to conventional geological mapping programmes

Hyperspectral-Augmented Target Tracking

With the global war on terrorism, the nature of military warfare has changed significantly. The United States Air Force is at the forefront of research and development in the field of intelligence, surveillance, and reconnaissance that provides American forces on the ground and in the air with the capability to seek, monitor, and destroy mobile terrorist targets in hostile territory. One such capability recognizes and persistently tracks multiple moving vehicles in complex, highly ambiguous urban environments. The thesis investigates the feasibility of augmenting a multiple-target tracking system with hyperspectral imagery. The research effort evaluates hyperspectral data classification using fuzzy c-means and the self-organizing map clustering algorithms for remote identification of moving vehicles. Results demonstrate a resounding 29.33% gain in performance from the baseline kinematic-only tracking to the hyperspectral-augmented tracking. Through a novel methodology, the hyperspectral observations are integrated in the MTT paradigm. Furthermore, several novel ideas are developed and implemented—spectral gating of hyperspectral observations, a cost function for hyperspectral observation-to-track association, and a self-organizing map filtering method. It appears that relatively little work in the target tracking and hyperspectral image classification literature exists that addresses these areas. Finally, two hyperspectral sensor modes are evaluated—Pushbroom and Region-of-Interest. Both modes are based on realistic technologies, and investigating their performance is the goal of performance-driven sensing. Performance comparison of the two modes can drive future design of hyperspectral sensors

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval