Angelos Tzotsos

Bio: Angelos Tzotsos is an academic researcher from National Technical University of Athens. The author has contributed to research in topics: Earth observation & Geospatial analysis. The author has an hindex of 9, co-authored 15 publications receiving 308 citations.

Support Vector Machine Classification for Object-Based Image Analysis

Abstract: The Support Vector Machine is a theoretically superior machine learning methodology with great results in pattern recognition. Especially for supervised classification of high-dimensional datasets and has been found competitive with the best machine learning algorithms. In the past, SVMs were tested and evaluated only as pixel-based image classifiers. During recent years, advances in Remote Sensing occurred in the field of Object-Based Image Analysis (OBIA) with combination of low level and high level computer vision techniques. Moving from pixel-based techniques towards object-based representation, the dimensions of remote sensing imagery feature space increases significantly. This results to increased complexity of the classification process, and causes problems to traditional classification schemes. The objective of this study was to evaluate SVMs for their effectiveness and prospects for object-based image analysis as a modern computational intelligence method. Here, an SVM approach for multi-class classification was followed, based on primitive image objects provided by a multi-resolution segmentation algorithm. Then, a feature selection step took place in order to provide the features for classification which involved spectral, texture and shape information. After the feature selection step, a module that integrated an SVM classifier and the segmentation algorithm was developed in C++. For training the SVM, sample image objects derived from the segmentation procedure were used. The proposed classification procedure followed, resulting in the final object classification. The classification results were compared to the Nearest Neighbor object-based classifier results, and were found satisfactory. The SVM methodology seems very promising for Object Based Image Analysis and future work will focus on integrating SVM classifiers with rule-based classifiers.

Object-based image analysis through nonlinear scale-space filtering

Abstract: In this research, an object-oriented image classification framework was developed which incorporates nonlinear scale-space filtering into the multi-scale segmentation and classification procedures. Morphological levelings, which possess a number of desired spatial and spectral properties, were associated with anisotropically diffused markers towards the construction of nonlinear scale spaces. Image objects were computed at various scales and were connected to a kernel-based learning machine for the classification of various earth-observation data from both active and passive remote sensing sensors. Unlike previous object-based image analysis approaches, the scale hierarchy is implicitly derived from scale-space representation properties. The developed approach does not require the tuning of any parameter—of those which control the multi-scale segmentation and object extraction procedure, like shape, color, texture, etc. The developed object-oriented image classification framework was applied on a number of remote sensing data from different airborne and spaceborne sensors including SAR images, high and very high resolution panchromatic and multispectral aerial and satellite datasets. The very promising experimental results along with the performed qualitative and quantitative evaluation demonstrate the potential of the proposed approach.

A hybrid texture-based and region-based multi-scale image segmentation algorithm

Abstract: The objective of this research was the design and development of a region-based multi-scale segmentation algorithm with the integration of complex texture features, in order to provide a low level processing tool for object-oriented image analysis. The implemented algorithm is called Texture-based MSEG and can be described as a region merging procedure. The first object representation is the single pixel of the image. Through iterative pair-wise object fusions, which are made at several iterations, called passes, the final segmentation is achieved. The criterion for object merging is a homogeneity cost measure, defined as object heterogeneity, and computed based on spectral and shape features for each possible object merge. An integration of texture features to the region merging segmentation procedure was implemented through an Advanced Texture Heuristics module. Towards this texture-enhanced segmentation method, complex statistical measures of texture had to be computed based on objects, however, and not on rectangular image regions. The approach was to compute grey level co-occurrence matrices for each image object and then to compute object-based statistical features. The Advanced Texture Heuristics module, integrated new heuristics in the decision for object merging, involving similarity measures of adjacent image objects, based on the computed texture features. The algorithm was implemented in C++ and was tested on remotely sensed images of different sensors, resolutions and complexity levels. The results were satisfactory since the produced primitive objects, were comparable to those of other segmentation algorithms. A comparison between the simple algorithm and the texture-based algorithm results showed that in addition to spectral and shape features, texture features did provide good segmentation results.

Geospatial Big Data for Environmental and Agricultural Applications

Abstract: Earth observation (EO) and environmental geospatial datasets are growing at an unprecedented rate in size, variety and complexity, thus, creating new challenges and opportunities as far as their access, archiving, processing and analytics are concerned. Currently, huge imaging streams are reaching several petabytes in many satellite archives worldwide. In this chapter, we review the current state-of-the-art in big data frameworks able to access, handle, process, analyse and deliver geospatial data and value-added products. Operational services that feature efficient implementations and different architectures allowing in certain cases the online and near real-time processing and analytics are detailed. Based on the current status, state-of-the-art and emerging challenges, the present study highlights certain issues, insights and future directions towards the efficient exploitation of EO big data for important engineering, environmental and agricultural applications.

Automatic extraction of physiographic features and alluvial fans in nevada, usa from digital elevation models and satellite imagery through multiresolution segmentation and object- oriented classification

Abstract: There is a need to automate terrain feature mapping so that to make the process more objective and less time consuming by using proper feature extraction techniques. The objective of this study was the use of object-oriented image analysis methods for the automatic extraction of physiographic regions and alluvial fan landform units. The study area was located in Nevada, USA. The data used included an ASTER L1 satellite image, the 1 o Digital Elevation Model and the GTOPO30 Digital Elevation Model, available by USGS. At first, a multiresolution segmentation algorithm was applied for extracting image primitives. A class hierarchy was defined in order to classify these primitives into semantic image objects. A fuzzy classification then provided the first approximations of three physiographic feature types (basins, piedmont slopes and mountains). Further processing, by a segment fusion technique, resulted in the reclassification of these image semantics into the final physiographic feature units. For the extraction of alluvial fan units, a multiresolution segmentation technique was developed, delivering object primitives at several resolution levels. At the finest level, the physiographic feature types were extracted from the DEM. At a medium level, a knowledge base including definitions of Alluvial Materials, Sediments, Basin Materials and Rock-Mountain Materials was implemented. This level was classified through several iterations, using spectral information for the first iteration of the classification procedure and heuristics concerning contextual information for the second iteration. Finally, at the coarse level, a projection was made, classifying the data into two classes: Alluvial Fans and Other Objects. The results were compared to manually produced maps by an expert geomorphologist and to computer-produced maps and they were found satisfactory.

A survey on object detection in optical remote sensing images

Abstract: Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective

Abstract: Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques.

Systems and methods for mobile image capture and processing

Abstract: In various embodiments, methods, systems, and computer program products for processing digital images captured by a mobile device are disclosed. Myriad features enable and/or facilitate processing of such digital images using a mobile device that would otherwise be technically impossible or impractical, and furthermore address unique challenges presented by images captured using a camera rather than a traditional flat-bed scanner, paper-feed scanner or multifunction peripheral.

Object detection in remote sensing imagery using a discriminatively trained mixture model

Abstract: Automatically detecting objects with complex appearance and arbitrary orientations in remote sensing imagery (RSI) is a big challenge. To explore a possible solution to the problem, this paper develops an object detection framework using a discriminatively trained mixture model. It is mainly composed of two stages: model training and object detection. In the model training stage, multi-scale histogram of oriented gradients (HOG) feature pyramids of all training samples are constructed. A mixture of multi-scale deformable part-based models is then trained for each object category by training a latent Support Vector Machine (SVM), where each part-based model is composed of a coarse root filter, a set of higher resolution part filters, and a set of deformation models. In the object detection stage, given a test imagery, its multi-scale HOG feature pyramid is firstly constructed. Then, object detection is performed by computing and thresholding the response of the mixture model. The quantitative comparisons with state-of-the-art approaches on two datasets demonstrate the effectiveness of the developed framework.