Comparative Performance Study and Analysis on Different Edge based Image Segmentation Techniques of Thermal Images

In this work, authors have been tried to analyse the edge-based approach for thermal image segmentation. Here, author?s have used different thermal images for the edge based analysis of image segmentation. Author?s have given studies regarding different edge operators like Prewitt, Sobel, LoG, and Canny edge detection operators for segmentation purposes and analyze their performance. This paper compares each of these operators by the manner of checking Peak signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) of resultant image. It evaluates the performance of each algorithm using image quality analysis. This paper presents a comparative analysis of different edge based thermal image segmentation techniques

Edge Detection in UAV Remote Sensing Images Using the Method Integrating Zernike Moments with Clustering Algorithms

Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy c-means (FCM) and K-means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivity methods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images

Compensating the lack of big data in construction industry with expert knowledge: a case study

Due to various reasons, there is a lack of big data in the construction industry, one of the main obstacles to a broader implementation of AI. Another obstacle is adhering to analytical methods in fields more suitable for AI solutions. If appropriately used, multidisciplinary expert knowledge can compensate for these problems and enhance the application of AI techniques in construction. The case study refers to rapid earthquake loss assessment. The problem with traditional systems is their low accuracy, making them unreliable and unusable in the recovery process, which is the purpose of loss assessment systems. Low accuracy is caused by too much uncertainty in analytical and insufficient data sets to create vulnerability curves in empirical methods. The contribution of this research is designing a new kind of rapid earthquake loss assessment system using multidisciplinary expert knowledge and AI methods. The problem of small data sets was solved using the procedure of representative sampling, which makes a small sample informative and sufficient to use. The low accuracy of analytical methods is caused by assuming theoretical vulnerability relations before an earthquake. The new approach uses trained assessors to perform on-the-ground observation of actual damage on the representative sample after an earthquake. AI methods are then used to predict damage to the remaining building portfolio, which is more accurate and still rapid enough. Another contribution is using a building representation without earthquake data which eliminates the need for analytical methods, shake maps and robust ground motion sensor networks, making the proposed framework unique and applicable in any regio

A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

Combined Edge Segment Texture Analysis for the Detection of Damaged Buildings in Crisis Areas

This paper describes the results of a new combined method that consists of a cooperative approach of several different algorithms for automated change detection. These methods are based on isotropic frequency filtering, spectral and texture analysis, and segmentation. For the frequency analysis, different band pass filters are applied to identify the relevant frequency information for change detection. After transforming the multitemporal images using a fast Fourier transform and applying the most suitable band pass filter to extract changed structures, we apply an edge detection algorithm in the spatial domain. For the texture analysis, we calculate the parameters energy and homogeneity for the multitemporal datasets. Then a principal component analysis is applied to the new multispectral texture images and subtracted to get the texture change information. This method can be combined with spectral information and prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination of the change algorithms is applied to calculate the probability of change for a particular location. This Combined Edge Segment Texture (CEST) method was tested with high-resolution remote-sensing images of the crisis area in Darfur (Sudan). Our results were compared with several standard algorithms for automated change detection, such as image difference, image ratio, principal component analysis, multivariate alteration detection (MAD) and post classification change detection. CEST showed superior accuracy compared to standard methods

Multiseasonal Remote Sensing of Vegetation with One-Class Classification – Possibilities and Limitations in Detecting Habitats of Nature Conservation Value

Mapping of habitats relevant for nature conservation often involves the identification of patches of target habitats in a complex mosaic of vegetation types extraneous for conservation planning. In field surveys, this is often a time-consuming and work-intensive task. Limiting the necessary ground reference to a small sample of target habitats and combining it with area-wide remote sensing data could greatly reduce and therefore support the field mapping effort. Conventional supervised classification methods need to be trained with a representative set of samples covering an exhaustive set of all classes. Acquiring such data is work intensive and hence inefficient in cases where only one or few classes are of interest. The usage of one-class classifiers (OCC) seems to be more suitable for this task – but has up until now neither been tested nor applied for large scale mapping and monitoring in programs such as those requested for the Natura 2000 European Habitat Directive or the High Nature Value (HNV) farmland Indicator. It is important to uncover the possibilities and mark the obstacles of this new approach since the usage of remote sensing for conservation purposes is currently controversially discussed in the ecology community as well as in the remote sensing community. Thus, the focal and innovative point of this thesis is to explore possibilities and limitations in the application of one-class classifiers for detecting habitats of nature conservation value with the help of multi-seasonal remote sensing and limited field data. The first study ascertains the usage of an OCC is suitable for mapping Natura 2000 habitat types. Applying the Maxent algorithm in combination with a low number of ground reference points of four habitat types and easily available multi-seasonal satellite imagery resulted in a combined habitat map with reasonable accuracy. There is potential in one-class classification for detecting rare habitats – however, differentiating habitats with very similar species composition remains challenging. Motivated by these positive results, the topic of the second study of this thesis is whether low and HNV grasslands can be differentiated with remotely-sensed reflectance data, field data and one-class classification. This approach could supplement existing field survey-based monitoring approaches such as for the HNV farmland Indicator. Three one-class classifiers together with multi-seasonal, multispectral remote sensing data in combination with sparse field data were analysed for their usage A) to classify HNV grassland against other areas and B) to differentiate between three quality classes of HNV grassland according to the current German HNV monitoring approach. Results indicated reasonable performances of the OCC to identify HNV grassland areas, but clearly showed that a separation into several HNV quality classes is not possible. In the third study the robustness and weak spots of an OCC were tested considering the effect of landscape composition and sample size on accuracy measurements. For this purpose artificial landscapes were generated to avoid the common problem of case-studies which usually can only make locally valid statements on the suitability of a tested approach. Whereas results concerning target sample size and the amount of similar classes in the background confirm conclusions of earlier studies from the field of species distribution modelling, results for background sample size and prevalence of target class give new insights and a basis for further studies and discussions. In conclusion the utilisation of an OCC together with reflectance and sparse field data for addressing rare vegetation types of conservation interest proves to be useful and has to be recommended for further research

The Impact of Sensor Characteristics and Data Availability on Remote Sensing Based Change Detection

Land cover and land use change are among the major drivers of global change. In a time of mounting challenges for sustainable living on our planet any research benefits from interdisciplinary collaborations to gain an improved understanding of the human-environment system and to develop suitable and improve existing measures of natural resource management. This includes comprehensive understanding of land cover and land use changes, which is fundamental to mitigate global change. Remote sensing technology is essential for the analyses of the land surface (and hence related changes) because it offers cost-effective ways of collecting data simultaneously over large areas. With increasing variety of sensors and better data availability, the application of remote sensing as a means to assist in modeling, to support monitoring, and to detect changes at various spatial and temporal scales becomes more and more feasible. The relationship between the nature of the changes on the land surface, the sensor properties, and the conditions at the time of acquisition influences the potential and quality of land cover and land use change detection. Despite the wealth of existing change detection research, there is a need for new methodologies in order to efficiently explore the huge amount of data acquired by remote sensing systems with different sensor characteristics. The research of this thesis provides solutions to two main challenges of remote sensing based change detection. First, geometric effects and distortions occur when using data taken under different sun-target-sensor geometries. These effects mainly occur if sun position and/or viewing angles differ between images. This challenge was met by developing a theoretical framework of bi-temporal change detection scenarios. The concept includes the quantification of distortions that can occur in unfavorable situations. The invention and application of a new method – the Robust Change Vector Analysis (RCVA) – reduced the detection of false changes due to these distortions. The quality and robustness of the RCVA were demonstrated in an example of bi-temporal cross-sensor change detection in an urban environment in Cologne, Germany. Comparison with a state-of-the-art method showed better performance of RCVA and robustness against thresholding. Second, this thesis provides new insights into how to optimize the use of dense time series for forest cover change detection. A collection of spectral indices was reviewed for their suitability to display forest structure, development, and condition at a study site on Vancouver Island, British Columbia, Canada. The spatio-temporal variability of the indices was analyzed to identify those indices, which are considered most suitable for forest monitoring based on dense time series. Amongst the indices, the Disturbance Index (DI) was found to be sensitive to the state of the forest (i.e., forest structure). The Normalized Difference Moisture Index (NDMI) was found to be spatio-temporally stable and to be the most sensitive index for changes in forest condition. Both indices were successfully applied to detect abrupt forest cover changes. Further, this thesis demonstrated that relative radiometric normalization can obscure actual seasonal variation and long-term trends of spectral signals and is therefore not recommended to be incorporated in the time series pre-processing of remotely-sensed data. The main outcome of this part of the presented research is a new method for detecting discontinuities in time series of spectral indices. The method takes advantage of all available information in terms of cloud-free pixels and hence increases the number of observations compared to most existing methods. Also, the first derivative of the time series was identified (together with the discontinuity measure) as a suitable variable to display and quantify the dynamic of dense Landsat time series that cannot be observed with less dense time series. Given that these discontinuities are predominantly related to abrupt changes, the presented method was successfully applied to clearcut harvest detection. The presented method detected major events of forest change at unprecedented temporal resolution and with high accuracy (93% overall accuracy). This thesis contributes to improved understanding of bi-temporal change detection, addressing image artifacts that result from flexible acquisition features of modern satellites (e.g., off-nadir capabilities). The demonstrated ability to efficiently analyze cross-sensor data and data taken under unfavorable conditions is increasingly important for the detection of many rapid changes, e.g., to assist in emergency response. This thesis further contributes to the optimized use of remotely sensed time series for improving the understanding, accuracy, and reliability of forest cover change detection. Additionally, the thesis demonstrates the usability of and also the necessity for continuity in medium spatial resolution satellite imagery, such as the Landsat data, for forest management. Constellations of recently launched (e.g., Landsat 8 OLI) and upcoming sensors (e.g., Sentinel-2) will deliver new opportunities to apply and extend the presented methodologies.Der Einfluss von Sensorcharakteristik und Datenverfügbarkeit auf die fernerkundungsbasierte Veränderungsdetektion Landbedeckungs- und Landnutzungswandel gehören zu den Haupttriebkräften des Globalen Wandels. In einer Zeit, in der ein nachhaltiges Leben auf unserem Planeten zu einer wachsenden Herausforderung wird, profitiert die Wissenschaft von interdisziplinärer Zusammenarbeit, um ein besseres Verständnis der Mensch-Umwelt-Beziehungen zu erlangen und um verbesserte Maßnahmen des Ressourcenmanagements zu entwickeln. Dazu gehört auch ein erweitertes Verständnis von Landbedeckungs- und Landnutzungswandel, das elementar ist, um dem Globalen Wandel zu begegnen. Die Fernerkundungstechnologie ist grundlegend für die Analyse der Landoberfläche und damit verknüpften Veränderungen, weil sie in der Lage ist, große Flächen gleichzeitig zu erfassen. Mit zunehmender Sensorenvielfalt und besserer Datenverfügbarkeit gewinnt Fernerkundung bei der Modellierung, beim Monitoring sowie als Mittel zur Erkennung von Veränderungen in verschiedenen räumlichen und zeitlichen Skalen zunehmend an Bedeutung. Das Wirkungsgeflecht zwischen der Art von Veränderungen der Landoberfläche, Sensoreigenschaften und Aufnahmebedingungen beeinflusst das Potenzial und die Qualität fernerkundungsbasierter Landbedeckungs- und Landnutzungsveränderungs-detektion. Trotz der Fülle an bestehenden Forschungsleistungen zur Veränderungsdetektion besteht ein dringender Bedarf an neuen Methoden, die geeignet sind, das große Aufkommen von Daten unterschiedlicher Sensoren effizient zu nutzen. Die in dieser Abschlussarbeit durchgeführte Forschung befasst sich mit zwei aktuellen Problemfeldern der fernerkundungsbasierten Veränderungsdetektion. Das erste sind die geometrischen Effekte und Verzerrungen, die auftreten, wenn Daten genutzt werden, die unter verschiedenen Sonne-Zielobjekt-Sensor-Geometrien aufgenommen wurden. Diese Effekte treten vor allem dann auf, wenn unterschiedliche Sonnenstände und/oder unterschiedliche Einfallswinkel der Satelliten genutzt werden. Der Herausforderung wurde begegnet, indem ein theoretisches Konzept von Szenarien dargelegt wurde, die bei der bi-temporalen Veränderungsdetektion auftreten können. Das Konzept beinhaltet die Quantifizierung der Verzerrungen, die in ungünstigen Fällen auftreten können. Um die Falscherkennung von Veränderungen in Folge der resultierenden Verzerrungen zu reduzieren, wurde eine neue Methode entwickelt – die Robust Change Vector Analysis (RCVA). Die Qualität der Methode wird an einem Beispiel der Veränderungsdetektion im urbanen Raum (Köln, Deutschland) aufgezeigt. Ein Vergleich mit einer anderen gängigen Methode zeigt bessere Ergebnisse für die neue RCVA und untermauert deren Robustheit gegenüber der Schwellenwertbestimmung. Die zweite Herausforderung, mit der sich die vorliegende Arbeit befasst, betrifft die optimierte Nutzung von dichten Zeitreihen zur Veränderungsdetektion von Wäldern. Eine Auswahl spektraler Indizes wurde hinsichtlich ihrer Tauglichkeit zur Erfassung von Waldstruktur, Waldentwicklung und Waldzustand in einem Untersuchungsgebiet auf Vancouver Island, British Columbia, Kanada, bewertet. Um die Einsatzmöglichkeiten der Indizes für dichte Zeitreihen bewerten zu können, wurde ihre raum-zeitliche Variabilität untersucht. Der Disturbance Index (DI) ist ein Index, der sensitiv für das Stadium eines Waldes ist (d. h. seine Struktur). DerNormalized Difference Moisture Index (NDMI) ist raum-zeitlich stabil und zudem am sensitivsten für Veränderungen des Waldzustands. Beide Indizes wurden erfolgreich zur Erkennung von abrupten Veränderungen getestet. In der vorliegenden Arbeit wird aufgezeigt, dass die relative radiometrische Normierung saisonale Variabilität und Langzeittrends von Zeitreihen spektraler Signale verzerrt. Die relative radiometrische Normierung wird daher nicht zur Vorprozessierung von Fernerkundungszeitreihen empfohlen. Das wichtigste Ergebnis dieser Studie ist eine neue Methode zur Erkennung von Diskontinuitäten in Zeitreihen spektraler Indizes. Die Methode nutzt alle wolkenfreien, ungestörten Beobachtungen (d. h. unabhängig von der Gesamtbewölkung in einem Bild) in einer Zeitreihe und erhöht dadurch die Anzahl an Beobachtungen im Vergleich zu anderen Methoden. Die erste Ableitung und die Messgröße zur Erfassung der Diskontinuitäten sind gut geeignet, um die Dynamik dichter Zeitreihen zu beschreiben und zu quantifizieren. Dies ist mit weniger dichten Zeitreihen nicht möglich. Da diese Diskontinuitäten im Untersuchungsgebiet üblicherweise abrupter Natur sind, ist die Methode gut geeignet, um Kahlschläge zu erfassen. Die hier dargelegte neue Methode detektiert Waldbedeckungsveränderungen mit einzigartiger zeitlicher Auflösung und hoher Genauigkeit (93% Gesamtgenauigkeit). Die vorliegende Arbeit trägt zu einem verbesserten Verständnis bi-temporaler Veränderungsdetektion bei, indem Bildartefakte berücksichtigt werden, die infolge der Flexibilität moderner Sensoren entstehen können. Die dargestellte Möglichkeit, Daten zu analysieren, die von unterschiedlichen Sensoren stammen und die unter ungünstigen Bedingungen aufgenommen wurden, wird zukünftig bei der Erfassung von schnellen Veränderungen an Bedeutung gewinnen, z. B. bei Katastropheneinsätzen. Ein weiterer Beitrag der vorliegenden Arbeit liegt in der optimierten Anwendung von Fernerkundungszeitreihen zur Verbesserung von Verständnis, Genauigkeit und Verlässlichkeit der Waldveränderungsdetektion. Des Weiteren zeigt die Arbeit den Nutzen und die Notwendigkeit der Fortführung von Satellitendaten mit mittlerer Auflösung (z. B. Landsat) für das Waldmanagement. Konstellationen kürzlich gestarteter (z. B. Landsat 8 OLI) und zukünftiger Sensoren (z. B. Sentinel-2) werden neue Möglichkeiten zur Anwendung und Optimierung der hier vorgestellten Methoden bieten