Gianni Cristian Iannelli

Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features

Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an “urban area” is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, “urban area” extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO) data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR) data prove the usefulness and flexibility of the framework.

Towards a free open source software tool for human settlement detection on VHR remote sensing images

This work presents the preliminary results of a joint enterprise aiming at the development of free open source software (FOSS) tools for very high resolution remote sensing (RS) image analysis (IA) at the global level. Specifically, the paper proposes a simple method for the detection of human settlements on RS images. The method relies on a model, which can be described in terms of few simple intuitive features related to the ontology of an urban area. In consequence, the method can be adjusted to different urban typologies by users with little background on RS and IA. Moreover, a FOSS implementation of the proposed method is described. The prototype integrates two free open source tools, namely Urban Focus and InterIMAGE. Both, method and prototype are tested experimentally on a CBERS dataset, whit encouraging results.

Hierarchical hybrid decision tree multiscale fusion for urban image classification

Multi-scale classification is an important tool for urban image classification, because the objects in an urban scene may have very different spatial scales. In technical literature, this idea is usually performed either using the same classifier (ensemble of classifiers) at multiple resolutions, or working on sets of features at multiple scales. Although this may be very efficient approach for peculiar situations, it is not as effective, as it does not adapt to different classes. Accordingly, it can be useful to design a procedure based on the possibility to assemble specialized (multi-scale) classification chains automatically, according to the classes to be recognized and their peculiar scales or features. We present a methodology that combines different processing chains (each one composed by a feature selection and a classification step) and automatically adapts to the properties of the classes available in an urban scene.

An Information Theory-Based Scheme for Efficient Classification of Remote Sensing Data

Information theory has recently become an interesting topic in earth observation data management and analysis, since it can provide important information on hidden interactions and correlations among the considered data records. Although several methods have been proposed and implemented to efficiently extract a proper set of features and deliver accurate image investigation, classification, and segmentation, these architectures show drawbacks when the data sets are characterized by complex interactions among the samples. In this paper, a new approach based on information theory for automatic pattern recognition is introduced for accurate classification of remotely sensed data. Experimental results carried out on real data sets show the validity of the proposed approach.

A feature-based approach to register CBERS CCD and HRC imagery for built-up area extraction purposes

The open data policy, the availability of high resolution imagery and the capability to cover fast-growing economies are among the main advantages of CBERS. Unfortunately, data produced by this satellite suffer of geographic misplacement, forcing to apply pre-processing techniques to stabilize the imagery. This paper introduces a feature-based technique developed to pre-process CBERS imagery over an area of interest. In particular, the algorithm is able to fix the shift among HRC high resolution (2.5 meters) and CCD medium resolution (20 meters). The final goal is to combine the advantages of high resolution and radiometric properties for built-up area extraction purposes.

Fusion of spectral and spatial features for human settlement extraction

The characterization of urban areas can be improved considerably by combining spectral and spatial features. As a matter of fact, depending on objects of interest in a specific application, the exploitation of both types of features at multiple spatial resolutions is required. This paper proposes a decision fusion method that relies on both spectral and textural features. The proposed approach is able to produce different classification results based on distinct partitions of the same input data set. Experiments conducted on CBERS-2B data demonstrate a significant performance improvement brought by the combination of spectral and textural features in comparison to the use of only spectral features to describe the image objects.

Improving the HBDT framework fusing HR and VHR SAR and optical data for image classification

Land cover mapping is usually characterized by a multiscale classification, generally because the image objects are better identified at multiple scales. Consequently, the features are usually extracted at different resolutions, and then classified by means of the same classifier (or a classifier ensemble). Unfortunately, this approach requires to select each time different scales for scenes containing (very) different classes. In these situations, a single multi-scale analysis may not be sufficient. The Hierarchical Binary Decision Tree (HBDT) approach, instead, combines different processing chains (composed by feature selection and classification steps) and automatically adapts to the spatial and spectral properties of the classes to be recognized in a scene. The HBDT algorithm has already proved to be efficient for single data sources. In this paper we propose two different data fusion techniques exploiting HBDT. These two techniques refer one to the fusion of results at the decision level, the other to fusion at the feature level. This work compares the performances of these two techniques using a data set of two city areas, i.e. Pavia (Italy) and Beijing (P.R. China), using HR and VHR optical and SAR data.

The IEEE GRSS data and algorithm standard evaluation (DASE) website: Incrementally building a standardized assessment for algorithm performance

In order to ensure homogeneity in performance assessment of proposed algorithms for information extraction in the Earth Observation (EO) domain, standardized remotely sensed datasets are particularly useful and welcome. Fully aware of this principle, the IEEE Geoscience and Remote Sensing Society (GRSS) and especially its Image Analysis and Data Fusion Technical Committee (IADF), has been organizing for some years now the Data Fusion Contest (DFC). In the DFC, one specific dataset is made available to the scientific community, which can download it and use it to test its newly developed algorithms. The consistence of the starting dataset across participating groups ensures the significance of assessing and ranking results, to finally proclaim the winner who scored the highest. More recently, the IEEE GRSS has provided one more contribution to the standardization effort by building the Data and Algorithm Standard Evaluation (DASE) website. DASE can distribute to registered users a limited set of possible “standard” open datasets, together with some ground truth info, and automatically assess the processing results provided by the users. In this paper we report on the birth of this initiative and present some recently introduced features.

Improving urban extent extraction from VHR optical data by means of cloud detection and image reconstruction

Mapping and monitoring urban area extents is a very relevant task for many applications related to risks, economic activities, population mapping and the interaction between people and the natural environment. This task can be accomplished using VHR and HR optical data. The main drawback of using the optical data is the irregular presence of the clouds, making mapping by these sensors completely useless in many cases. Even when the clouds are small and irregular, the extracted urban area extents may be erroneous and/or incomplete, and many uncertainties show up in thematic maps. In this paper, we prove that the joint use of algorithms for urban extent extraction, cloud detection and masking, and image reconstruction provide a clear processing chain able to overcome this issue and to increasing the reliability of the extracted urban area extents.

A landscape archaeology application of “big heritage data”: Detecting traces of Roman centuriations in large-scale, old aerial photos

Centuriation was a regular method for land parcel definition used by the ancient Romans, whose vestiges are still visible today in some places. Detecting such vestiges is relevant to landscape archaeology studies. In this paper we propose a new method for extensive automated inspection of heritage aerial photos in order to detect clues of centuriation.