bluuebunny/crossref_metadata_2025 huggingface dataset:
EVALUATING THE POTENTIAL OF RTK-UAV FOR AUTOMATIC POINT CLOUD GENERATION IN 3D RAPID MAPPING
Authors: Fazeli H. , Samadzadegan F. , Dadrasjavan F.
Published: August 2016

During disaster and emergency situations, 3D geospatial data can provide essential information for decision support systems. The utilization of geospatial data using digital surface models as a basic reference is mandatory to provide accurate quick emergency response in so called rapid mapping activities. The recipe between accuracy requirements and time restriction is considered critical in this situations. UAVs as alternative platforms for 3D point cloud acquisition offer potentials because of their flexibility and practicability combined with low cost implementations. Moreover, the high resolution data collected from UAV platforms have the capabilities to provide a quick overview of the disaster area. The target of this paper is to experiment and to evaluate a low-cost system for generation of point clouds using imagery collected from a low altitude small autonomous UAV equipped with customized single frequency RTK module. The customized multi-rotor platform is used in this study. Moreover, electronic hardware is used to simplify user interaction with the UAV as RTK-GPS/Camera synchronization, and beside the synchronization, lever arm calibration is done. The platform is equipped with a Sony NEX-5N, 16.1-megapixel camera as imaging sensor. The lens attached to camera is ZEISS optics, prime lens with F1.8 maximum aperture and 24 mm focal length to deliver outstanding images. All necessary calibrations are performed and flight is implemented over the area of interest at flight height of 120 m above the ground level resulted in 2.38 cm GSD. Earlier to image acquisition, 12 signalized GCPs and 20 check points were distributed in the study area and measured with dualfrequency GPS via RTK technique with horizontal accuracy of σ = 1.5 cm and vertical accuracy of σ = 2.3 cm. results of direct georeferencing are compared to these points and experimental results show that decimeter accuracy level for 3D points cloud with proposed system is achievable, that is suitable for 3D rapid mapping applications.
STATISTICS FOR PATCH OBSERVATIONS
Authors: Hingee K. L.
Published: August 2016

In the application of remote sensing it is common to investigate processes that generate patches of material. This is especially true when using categorical land cover or land use maps. Here we view some existing tools, landscape pattern indices (LPI), as non-parametric estimators of random closed sets (RACS). This RACS framework enables LPIs to be studied rigorously. A RACS is any random process that generates a closed set, which encompasses any processes that result in binary (two-class) land cover maps. RACS theory, and methods in the underlying field of stochastic geometry, are particularly well suited to high-resolution remote sensing where objects extend across tens of pixels, and the shapes and orientations of patches are symptomatic of underlying processes. For some LPI this field already contains variance information and border correction techniques. After introducing RACS theory we discuss the core area LPI in detail. It is closely related to the spherical contact distribution leading to conditional variants, a new version of contagion, variance information and multiple border-corrected estimators. We demonstrate some of these findings on high resolution tree canopy data.
ASSESSING 3D PHOTOGRAMMETRY TECHNIQUES IN CRANIOMETRICS
Authors: Moshobane M. C. , de Bruyn P. J. N. , Bester M. N.
Published: August 2016

Morphometrics (the measurement of morphological features) has been revolutionized by the creation of new techniques to study how organismal shape co-varies with several factors such as ecophenotypy. Ecophenotypy refers to the divergence of phenotypes due to developmental changes induced by local environmental conditions, producing distinct ecophenotypes. None of the techniques hitherto utilized could explicitly address organismal shape in a complete biological form, i.e. three-dimensionally. This study investigates the use of the commercial software, Photomodeler Scanner® (PMSc®) three-dimensional (3D) modelling software to produce accurate and high-resolution 3D models. Henceforth, the modelling of Subantarctic fur seal (Arctocephalus tropicalis) and Antarctic fur seal (Arctocephalus gazella) skulls which could allow for 3D measurements. Using this method, sixteen accurate 3D skull models were produced and five metrics were determined. The 3D linear measurements were compared to measurements taken manually with a digital caliper. In addition, repetitive measurements were recorded by varying researchers to determine repeatability. To allow for comparison straight line measurements were taken with the software, assuming that close accord with all manually measured features would illustrate the model’s accurate replication of reality. Measurements were not significantly different demonstrating that realistic 3D skull models can be successfully produced to provide a consistent basis for craniometrics, with the additional benefit of allowing non-linear measurements if required.
Learning Photogrammetry with Interactive Software Tool PhoX
Authors: Luhmann T.
Published: August 2016

Photogrammetry is a complex topic in high-level university teaching, especially in the fields of geodesy, geoinformatics and metrology where high quality results are demanded. In addition, more and more black-box solutions for 3D image processing and point cloud generation are available that generate nice results easily, e.g. by structure-from-motion approaches. Within this context, the classical approach of teaching photogrammetry (e.g. focusing on aerial stereophotogrammetry) has to be reformed in order to educate students and professionals with new topics and provide them with more information behind the scene. Since around 20 years photogrammetry courses at the Jade University of Applied Sciences in Oldenburg, Germany, include the use of digital photogrammetry software that provide individual exercises, deep analysis of calculation results and a wide range of visualization tools for almost all standard tasks in photogrammetry. During the last years the software package PhoX has been developed that is part of a new didactic concept in photogrammetry and related subjects. It also serves as analysis tool in recent research projects. PhoX consists of a project-oriented data structure for images, image data, measured points and features and 3D objects. It allows for almost all basic photogrammetric measurement tools, image processing, calculation methods, graphical analysis functions, simulations and much more.

 Students use the program in order to conduct predefined exercises where they have the opportunity to analyse results in a high level of detail. This includes the analysis of statistical quality parameters but also the meaning of transformation parameters, rotation matrices, calibration and orientation data. As one specific advantage, PhoX allows for the interactive modification of single parameters and the direct view of the resulting effect in image or object space.
AN ADAPTIVE WEB-BASED LEARNING ENVIRONMENT FOR THE APPLICATION OF REMOTE SENSING IN SCHOOLS
Authors: Wolf N. , Fuchsgruber V. , Riembauer G. , Siegmund A.
Published: August 2016

Satellite images have great educational potential for teaching on environmental issues and can promote the motivation of young people to enter careers in natural science and technology. Due to the importance and ubiquity of remote sensing in science, industry and the public, the use of satellite imagery has been included into many school curricular in Germany. However, its implementation into school practice is still hesitant, mainly due to lack of teachers’ know-how and education materials that align with the curricula. In the project “Space4Geography” a web-based learning platform is developed with the aim to facilitate the application of satellite imagery in secondary school teaching and to foster effective student learning experiences in geography and other related subjects in an interdisciplinary way. The platform features ten learning modules demonstrating the exemplary application of original high spatial resolution remote sensing data (RapidEye and TerraSAR-X) to examine current environmental issues such as droughts, deforestation and urban sprawl. In this way, students will be introduced into the versatile applications of spaceborne earth observation and geospatial technologies. The integrated web-based remote sensing software “BLIF” equips the students with a toolset to explore, process and analyze the satellite images, thereby fostering the competence of students to work on geographical and environmental questions without requiring prior knowledge of remote sensing. This contribution presents the educational concept of the learning environment and its realization by the example of the learning module “Deforestation of the rainforest in Brasil”.
IMPLEMENTATION OF ACTIVE TEACHING METHODS AND EMERGING TOPICS IN PHOTOGRAMMETRY AND REMOTE SENSING SUBJECTS
Authors: Kosmatin Fras M. , Grigillo D.
Published: August 2016

Fast technological developments in photogrammetry and remote sensing areas demand quick and steady changes in the education programme and its realization. The university teachers and assistants are faced with ensuring the learning materials, data and software for practical lessons, as well as project proposals for student’s team work and bachelor or master thesis. In this paper the emerging topics that already have a considerable impact in the practice are treated mostly from the educational aspect. These relatively new topics that are considered in this paper are unmanned aerial systems for spatial data collection, terrestrial and aerial laser scanning, mobile mapping systems, and novelties in satellite remote sensing. The focus is given to practical implementation of these topics into the teaching and learning programme of Geodesy and Geoinformation at the University of Ljubljana, Faculty of Civil and Geodetic Engineering, and experiences gained by the authors so far. Together with the technological advances, the teaching approaches must be modernized as well. Classical approaches of teaching, where a lecturer gives lecture ex cathedra and students are only listeners, are not effective enough. The didactics science of teaching has developed and proved in the practice many useful approaches that can better motivate students for more active learning. We can use different methods of team work like pro et contra debate, buzzing groups, press conference, moderated discussion etc. An experimental study on active teaching methods in the class of students of the Master programme of Geodesy and Geoinformation has been made and the results are presented. After using some new teaching methods in the class, the students were asked to answer two types of a questionnaire. First questionnaire was the standard form developed by Noel Entwistle, an educational psychologist who developed the Approaches to Studying Inventory (ASI) for identifying deep and surface approaches to learning. The second questionnaire was developed for our purpose to get the feedback from students on active teaching and learning methods. Although this investigation has been done only for one class of master programme students, the results are encouraging and we could extract some recommendations for the future.
HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS
Authors: Rejas Ayuga J. G. , Martínez Marín R. , Marchamalo Sacristán M. , Bonatti J. , Ojeda J. C.
Published: August 2016

We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain) and San José (Costa Rica) respectively, have been used.

 In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background) based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.
A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE SPECTRAL COMPARISON
Authors: Sharifi hashjin S. , Darvishi A. , Khazai S. , Hatami F. , Jafari houtki M.
Published: August 2016

In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.
Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data
Authors: Wang Lijuan , Zhang Guimin , Lin Hui , Liang Liang , Niu Zheng
Published: August 2016

The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.
ASSESSMENT OF MULTIRESOLUTION SEGMENTATION FOR EXTRACTING GREENHOUSES FROM WORLDVIEW-2 IMAGERY
Authors: Aguilar M. A. , Aguilar F. J. , García Lorca A. , Guirado E. , Betlej M. , Cichon P. , Nemmaoui A. , Vallario A. , Parente C.
Published: August 2016

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR satellite imagery. OBIA considers spectral, geometric, textural and topological attributes associated with meaningful image objects. Thus, the first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from WorldView- 2 multispectral orthoimages. Specifically, the focus is on finding the optimal parameters of the multiresolution segmentation approach (i.e., Scale, Shape and Compactness) for plastic greenhouses. The optimum Scale parameter estimation was based on the idea of local variance of object heterogeneity within a scene (ESP2 tool). Moreover, different segmentation results were attained by using different combinations of Shape and Compactness values. Assessment of segmentation quality based on the discrepancy between reference polygons and corresponding image segments was carried out to identify the optimal setting of multiresolution segmentation parameters. Three discrepancy indices were used: Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR) and Euclidean Distance 2 (ED2).
DETECTION OF BARCHAN DUNES IN HIGH RESOLUTION SATELLITE IMAGES
Authors: Azzaoui M. A. , Adnani M. , El Belrhiti H. , Chaouki I. E. , Masmoudi C.
Published: August 2016

Barchan dunes are the fastest moving sand dunes in the desert. We developed a process to detect barchans dunes on High resolution satellite images. It consisted of three steps, we first enhanced the image using histogram equalization and noise reduction filters. Then, the second step proceeds to eliminate the parts of the image having a texture different from that of the barchans dunes. Using supervised learning, we tested a coarse to fine textural analysis based on Kolomogorov Smirnov test and Youden’s J-statistic on co-occurrence matrix. As an output we obtained a mask that we used in the next step to reduce the search area. In the third step we used a gliding window on the mask and check SURF features with SVM to get barchans dunes candidates. Detected barchans dunes were considered as the fusion of overlapping candidates. The results of this approach were very satisfying in processing time and precision.
OBJECT-BASED GREENHOUSE CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGERY: A CASE STUDY ANTALYA-TURKEY
Authors: Coslu M. , Sonmez N. K. , Koc-San D.
Published: August 2016

Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segmentation and updatable rule sets. In this study, it was aimed to detect and map the greenhouses from object-based classification method by using high resolution satellite imagery. The study was carried out in the Antalya province which includes greenhouse intensively. The study consists of three main stages including segmentation, classification and accuracy assessment. At the first stage, which was segmentation, the most important part of the object-based imagery analysis; imagery segmentation was generated by using basic spectral bands of high resolution Worldview-2 satellite imagery. At the second stage, applying the nearest neighbour classifier to these generated segments classification process was executed, and a result map of the study area was generated. Finally, accuracy assessments were performed using land studies and digital data of the area. According to the research results, object-based greenhouse classification using high resolution satellite imagery had over 80% accuracy.
THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM) USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES
Authors: Cubillas J. E. , Japitana M.
Published: August 2016

This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a) Blk66L005, (b) Blk66L021, (c) Blk66L024, and (d) Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier) rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.
AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION
Authors: Ding Hu , Tao Fei , Zhao Wufan , Na Jiaming , Tang Guo’an
Published: August 2016

Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.
ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES
Authors: Hsieh Yi-Ta , Wu Shou-Tsung , Chen Chaur-Tzuhn , Chen Jan-Chang
Published: August 2016

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.
MONITORING OF LAND SUBSIDENCE IN RAVENNA MUNICIPALITY USING INTEGRATED SAR - GPS TECHNIQUES: DESCRIPTION AND FIRST RESULTS
Authors: Artese G. , Fiaschi S. , Di Martire D. , Tessitore S. , Fabris M. , Achilli V. , Ahmed A. , Borgstrom S. , Calcaterra D. , Ramondini M. , Artese S. , Floris M. , Menin A. , Monego M. , Siniscalchi V.
Published: August 2016

The Emilia Romagna Region (N-E Italy) and in particular the Adriatic Sea coastline of Ravenna, is affected by a noticeable subsidence that started in the 1950s, when the exploitation of on and off-shore methane reservoirs began, along with the pumping of groundwater for industrial uses. In such area the current subsidence rate, even if lower than in the past, reaches the -2 cm/y. Over the years, local Authorities have monitored this phenomenon with different techniques: spirit levelling, GPS surveys and, more recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, confirming the critical situation of land subsidence risk. In this work, we present the comparison between the results obtained with DInSAR and GPS techniques applied to the study of the land subsidence in the Ravenna territory. With regard to the DInSAR, the Small Baseline Subset (SBAS) and the Coherent Pixel Technique (CPT) techniques have been used. Different SAR datasets have been exploited: ERS-1/2, ENVISAT, TerraSAR-X and Sentinel-1. Some GPS campaigns have been also carried out in a subsidence prone area. 3D vertices have been selected very close to existing persistent scatterers in order to link the GPS measurement results to the SAR ones. GPS data were processed into the International reference system and the comparisons between the coordinates, for the first 6 months of the monitoring, provided results with the same trend of the DInSAR data, even if inside the precision of the method.
COMPARISON OF FILTERS DEDICATED TO SPECKLE SUPPRESSION IN SAR IMAGES
Authors: Kupidura P.
Published: August 2016

This paper presents the results of research on the effectiveness of different filtering methods dedicated to speckle suppression in SAR images. The tests were performed on RadarSat-2 images and on an artificial image treated with simulated speckle noise. The research analysed the performance of particular filters related to the effectiveness of speckle suppression and to the ability to preserve image details and edges. Speckle is a phenomenon inherent to radar images – a deterministic noise connected with land cover type, but also causing significant changes in digital numbers of pixels. As a result, it may affect interpretation, classification and other processes concerning radar images. Speckle, resembling “salt and pepper” noise, has the form of a set of relatively small groups of pixels of values markedly different from values of other pixels representing the same type of land cover. Suppression of this noise may also cause suppression of small image details, therefore the ability to preserve the important parts of an image, was analysed as well. In the present study, selected filters were tested, and methods dedicated particularly to speckle noise suppression: Frost, Gamma-MAP, Lee, Lee-Sigma, Local Region, general filtering methods which might be effective in this respect: Mean, Median, in addition to morphological filters (alternate sequential filters with multiple structuring element and by reconstruction). The analysis presented in this paper compared the effectiveness of different filtering methods. It proved that some of the dedicated radar filters are efficient tools for speckle suppression, but also demonstrated a significant efficiency of the morphological approach, especially its ability to preserve image details.
LAND USE CLASSIFICATION FROM VHR AERIAL IMAGES USING INVARIANT COLOUR COMPONENTS AND TEXTURE
Authors: Movia A. , Beinat A. , Sandri T.
Published: August 2016

Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available.

 In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element.

 To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications.

 The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.
INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION
Authors: Wang Chunyan , Xu Aigong , Li Chao , Zhao Xuemei
Published: August 2016

Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).
REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA
Authors: Yu Xin , Wen Zongyong , Zhu Zhaorong , Xia Qiang , Shun Lan
Published: August 2016

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.
S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES
Authors: Zhang Ruiqian , Yao Jian , Zhang Kao , Feng Chen , Zhang Jiadong
Published: August 2016

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.
AN ENTROPY-KL STRATEGY FOR ESTIMATING NUMBER OF CLASSES IN IMAGE SEGMENTATION ISSUES
Authors: Zhao Xuemei , Li Yu , Zhao Quanhua , Wang Chunyan
Published: August 2016

different classes, to estimate the number of classes in image segmentation issues. In this strategy, the information of a homogeneous region is measured by entropy. Then a region is considered to be disordered and should be split if its entropy is more than a given threshold. On the contrary, when the KL information of two homogeneous regions is less than a threshold, it is believed that they are similar and should be merged. The entropy-KL strategy can be combined with any kind of segmentation algorithm since it uses the information and distance as a general way to decide the number of classes. In this paper, the HMRF-FCM algorithm is employed as the segmentation process and combined with the entropy-KL strategy to induce a segmentation algorithm which can fix the number of classes automatically. The proposed algorithm is performed on synthetic image, real panchromatic images and SAR images to demonstrate the effectiveness.
SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY
Authors: Zhu Qiqi , Zhong Yanfei , Zhang Liangpei
Published: August 2016

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.
A NEW IDL IMPLEMENTATION OF THE JUPP METHOD FOR BATHYMETRY EXTRACTION IN SHALLOW WATERS
Authors: Deidda M. , Pala A. , Sanna G.
Published: August 2016

There is an ongoing effort in using imagery from remote sensing platforms to obtain information about the sea depth; this allows to monitor the dynamics of coastal erosion without the need for costly and repeated local surveys. We worked on a new implementation of the Jupp method to extract depth information from satellite images. Our software is based on previous implementations of the algorithm in the IDL language, but we made our current implementation more modular in order to make possible experimentations with different approaches. We used this implementation on a series of six images (three from the Landsat TM sensor and three from the Landsat OLI sensor) in order to improve the available tools. We established an iterative workflow for working on the Landat-8 images widely exposed in this paper.
GROUND DEFORMATION EXTRACTION USING VISIBLE IMAGES AND LIDAR DATA IN MINING AREA
Authors: Hu Wenmin , Wu Lixin
Published: August 2016

Recognition and extraction of mining ground deformation can help us understand the deformation process and space distribution, and estimate the deformation laws and trends. This study focuses on the application of ground deformation detection and extraction combining with high resolution visible stereo imagery, LiDAR observation point cloud data and historical data. The DEM in large mining area is generated using high-resolution satellite stereo images, and ground deformation is obtained through time series analysis combined with historical DEM data. Ground deformation caused by mining activities are detected and analyzed to explain the link between the regional ground deformation and local deformation. A district of covering 200 km2 around the West Open Pit Mine in Fushun of Liaoning province, a city located in the Northeast China is chosen as the test area for example. Regional and local ground deformation from 2010 to 2015 time series are detected and extracted with DEMs derived from ZY-3 images and LiDAR point DEMs in the case study. Results show that the mean regional deformation is 7.1 m of rising elevation with RMS 9.6 m. Deformation of rising elevation and deformation of declining elevation couple together in local area. The area of higher elevation variation is 16.3 km2 and the mean rising value is 35.8 m with RMS 15.7 m, while the deformation area of lower elevation variation is 6.8 km2 and the mean declining value is 17.6 m with RMS 9.3 m. Moreover, local large deformation and regional slow deformation couple together, the deformation in local mining activities has expanded to the surrounding area, a large ground fracture with declining elevation has been detected and extracted in the south of West Open Pit Mine, the mean declining elevation of which is 23.1 m and covering about 2.3 km2 till 2015. The results in this paper are preliminary currently; we are making efforts to improve more precision results with invariant ground control data for validation.
OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH
Authors: Jia Yonghong , Zhou Mingting , Jinshan Ye
Published: August 2016

The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.
MONITORING SOIL MOISTURE IN A COAL MINING AREA WITH MULTI-PHASE LANDSAT IMAGES
Authors: Kong J. L. , Xian T. , Yang J. , Chen L. , Yang X. T.
Published: August 2016

The coal development zone of Northern Shaanxi, China is one of the eight largest coal mines in the world, also the national energy and chemical bases. However, the coal mining leads to ground surface deformation and previous studies show that in collapse fissure zone soil water losses almost 50% compared with non-fissure zone. The main objective of this study is to develop a retrieval model that is reliable and sensitive to soil moisture in the whole coal mining zone of Northern Shaanxi based upon the soil sample parameters collected from in situ site investigation, spectral data gathered simultaneously and the images of Landsat7 ETM. The model uses different phases of Landsat data to retrieve soil moisture and analyze the patterns of spatial and temporal variations of soil moisture caused by ground deformation in the coal mining areas. The study indicated that band4 of Landsat7 ETM is the most sensitive band for soil moisture retrieval using the spectrum method. The quadratic model developed by remote sensing reflectance (Rrs4) (corresponding to the band4) is the best pattern with the correlation coefficient of 0.858 between the observed and the estimated soil moisture. Two-phase Landsat7 ETM data of 2002 and 2009 and one phase Landsat8 OLI data of 2015 for the study area were selected to retrieve soil moisture information. The result showed that the mean relative error was 35.16% and the root-mean-squared error (RMSE) was 0.58%. The changes of the spatial distribution of inversed soil moisture revealed that the trend of soil moisture contents of the study area was in general being gradually reduced from 2002 to 2015. The study results can serve as the baseline for monitoring environmental impacts on soil moisture in the regions due to coal mining.
COMPARISON OF C-BAND AND X-BAND POLARIMETRIC SAR DATA FOR RIVER ICE CLASSIFICATION ON THE PEACE RIVER
Authors: Łoś H. , Osińska-Skotak K. , Pluto-Kossakowska J. , Bernier M. , Gauthier Y. , Jasek M. , Roth A.
Published: August 2016

In this study, synthetic aperture radar (SAR) data from TerraSAR-X were compared with RADARSAT-2 data to evaluate their effectiveness for river ice monitoring on the Peace River. For several years RADARSAT-2 data have been successfully used for river ice observation. However, it is important to take into account data from other satellites as they may provide solutions when it is not possible to obtain images from the preferred system (e.g., in the case of acquisition priority conflicts). In this study we compared three TerraSAR-X (X-band) and three RADARSAT-2 (C-band) datasets acquired in December 2013 on a section of the Peace River, Canada. For selected classes (open water, skim ice, juxtaposed skim ice, agglomerated skim ice, frazil run and consolidated ice) we compared backscattering values in HH and VV polarisation and performed Wishart supervised classification. Covariance matrices that were previously filtered using a refined Lee filter were used as input data for classification. For all data sets the overall accuracy was higher than 80%. Similar errors associated with classification output were observed for data from both satellite systems.
MONITORING GROUND SUBSIDENCE IN AREAS COVERED BY DENSE VEGETATION USING TERRASAR-X IMAGES: A CASE STUDY OF HANGZHOU
Authors: Wu H. A. , Zhang Y. H. , Luo G. F. , Kang Y. K. , Zhu Y. M.
Published: August 2016

Hangzhou, the capital of Zhejiang province has suffered serious ground subsidence during the past several decades, due to long term over-exploration of groundwater. In this paper, the time series InSAR technique using high resolution SAR images is investigated for the generation of subsidence maps over Hangzhou region. 29 TerraSAR-X images acquired from May 2012 to Sep 2015 are used. The results show that serious subsidence has mainly taken place in suburban area, including Yuhang district, Xiaoshan district and Binjiang district. 4 subsidence centers are discovered, namely Tangqi town in Yuhang with an average subsiding velocity of -29.6 mm/year, Xintang (-30.7 mm/year) in Xiaoshan, Zhujiaqiao town (-25.6mm/year) in Xiaoshan, and Miaohouwang town (-30.1mm/year) in Binjiang. The urban area is stable and ground rebound even take place in some places. The results are compared with 19 levelling measurements. The RMS error between them is 2.9 mm/year, which demonstrates that the high resolution TerraSAR-X images has good accuracy for subsidence monitoring in the southeast of China, covered by dense vegetation.