Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory

Land cover classification using multispectral satellite image is a very challenging task with numerous practical applications. We propose a multi-stage classifier that involves fuzzy rule extraction from the training data and then generation of a possibilistic label vector for each pixel using the fuzzy rule base. To exploit the spatial correlation of land cover types we propose four different information aggregation methods which use the possibilistic class label of a pixel and those of its eight spatial neighbors for making the final classification decision. Three of the aggregation methods use Dempster-Shafer theory of evidence while the remaining one is modeled after the fuzzy k-NN rule. The proposed methods are tested with two benchmark seven channel satellite images and the results are found to be quite satisfactory. They are also compared with a Markov random field (MRF) model-based contextual classification method and found to perform consistently better.Comment: 14 pages, 2 figure

Full Hierarchic Versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data

Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84

A New Fuzzy Clustering Algorithm Based on Clonal Selection for Land Cover Classification

A new fuzzy clustering algorithm based on clonal selection theory from artificial immune systems (AIS), namely, FCSA, is proposed to obtain the optimal clustering result of land cover classification without a priori assumptions on the number of clusters. FCSA can adaptively find the optimal number of clusters and is designed as a two-layer system: the classification layer and the optimization layer. The classification layer of FCSA, inspired by clonal selection theory, generates the optimal classification result with a fixed cluster number by utilizing the clone, mutation, and selection of immune operators. The optimization layer of FCSA evaluates the optimal solutions according to performance measures for cluster validity and then adjusts the cluster number to output the final optimal cluster number. Two experiments with different types of image evince that FCSA not only finds the optimal number of clusters, but also consistently outperforms the traditional clustering algorithms, such as K-means and Fuzzy C-means. Hence, FCSA provides an effective option for performing the task of land cover classification

CNN기반의 FusionNet 신경망과 농지 경계추출 알고리즘을 이용한 토지피복분류모델 개발

학위논문 (석사) -- 서울대학교 대학원 : 농업생명과학대학 생태조경.지역시스템공학부(지역시스템공학전공), 2021. 2. 송인홍.토지이용이 빠르게 변화함에 따라, 토지 피복에 대한 공간정보를 담고 있는 토지 피복 지도의 신속한 최신화는 필수적이다. 하지만, 현 토지 피복 지도는 많은 시간과 노동력을 요구하는 manual digitizing 방법으로 제작됨에 따라, 토지 피복 지도의 업데이트 및 배포에 긴 시간 간격이 발생하는 실정이다. 본 연구에서는 convolutional neural network (CNN) 기반의 인공신경망을 이용하여 high-resolution remote sensing (HRRS) 영상으로부터 토지 피복을 분류하는 모델을 개발하고, 특히 농지 경계추출 알고리즘을 적용하여 농업지역에서 분류 정확도를 개선하고자 하였다. 개발된 토지 피복 분류모델은 전처리(pre-processing) 모듈, 토지 피복 분류(land cover classification) 모듈, 그리고 후처리(post-processing) 모듈의 세 모듈로 구성된다. 전처리 모듈은 입력된 HRRS 영상을 75%씩 중첩 분할하여 관점을 다양화하는 모듈로, 한 관점에서 토지 피복을 분류할 때 발생할 수 있는 오분류를 줄이고자 하였다. 토지 피복 분류 모듈은 FusionNet model 구조를 바탕으로 개발되었고, 이는 분할된 HRRS 이미지의 픽셀별로 최적 토지 피복을 부여하도록 설계되었다. 후처리 모듈은 픽셀별 최종 토지 피복을 결정하는 모듈로, 분할된 HRRS 이미지의 분류결과를 취합하여 최빈값을 최종 토지 피복으로 결정한다. 추가로 농지에서는 농지경계를 추출하고, 필지별 분류된 토지 피복을 집계하여 한 필지에 같은 토지 피복을 부여하였다. 개발된 토지 피복 분류모델은 전라남도 지역(면적: 547 km2)의 2018년 정사영상과 토지 피복 지도를 이용하여 학습되었다. 토지 피복 분류모델 검증은 학습지역과 시간, 공간적으로 구분된, 2018년 전라남도 수북면과 2016년 충청북도 대소면의 두 검증지역에서 수행되었다. 각 검증지역에서 overall accuracy는 0.81, 0.71로 집계되었고, kappa coefficients는 0.75, 0.64로 산정되어 substantial 수준의 토지 피복 분류 정확도를 확인하였다. 특히, 개발된 모델은 필지 경계를 고려한 농업지역에서 overall accuracy 0.89, kappa coefficient 0.81로 almost perfect 수준의 우수한 분류 정확도를 보였다. 이에 개발된 토지 피복 분류모델은 특히 농업지역에서 현 토지 피복 분류 방법을 지원하여 토지 피복 지도의 빠르고 정확한 최신화에 기여할 수 있을 것으로 생각된다.The rapid update of land cover maps is necessary because spatial information of land cover is widely used in various areas. However, these maps have been released or updated in the interval of several years primarily owing to the manual digitizing method, which is time-consuming and labor-intensive. This study was aimed to develop a land cover classification model using the concept of a convolutional neural network (CNN) that classifies land cover labels from high-resolution remote sensing (HRRS) images and to increase the classification accuracy in agricultural areas using the parcel boundary extraction algorithm. The developed model comprises three modules, namely the pre-processing, land cover classification, and post-processing modules. The pre-processing module diversifies the perspective of the HRRS images by separating images with 75% overlaps to reduce the misclassification that can occur in a single image. The land cover classification module was designed based on the FusionNet model structure, and the optimal land cover type was assigned for each pixel of the separated HRRS images. The post-processing module determines the ultimate land cover types for each pixel unit by summing up the several-perspective classification results and aggregating the pixel-classification result for the parcel-boundary unit in agricultural areas. The developed model was trained with land cover maps and orthographic images (area: 547 km2) from the Jeonnam province in Korea. Model validation was conducted with two spatially and temporally different sites including Subuk-myeon of Jeonnam province in 2018 and Daseo-myeon of Chungbuk province in 2016. In the respective validation sites, the models overall accuracies were 0.81 and 0.71, and kappa coefficients were 0.75 and 0.64, implying substantial model performance. The model performance was particularly better when considering parcel boundaries in agricultural areas, exhibiting an overall accuracy of 0.89 and kappa coefficient 0.81 (almost perfect). It was concluded that the developed model may help perform rapid and accurate land cover updates especially for agricultural areas.Chapter 1. Introduction 1 1.1. Study background 1 1.2. Objective of thesis 4 Chapter 2. Literature review 6 2.1. Development of remote sensing technique 6 2.2. Land cover segmentation 9 2.3. Land boundary extraction 13 Chapter 3. Development of the land cover classification model 15 3.1. Conceptual structure of the land cover classification model 15 3.2. Pre-processing module 16 3.3. CNN based land cover classification module 17 3.4. Post processing module 22 3.4.1 Determination of land cover in a pixel unit 22 3.4.2 Aggregation of land cover to parcel boundary 24 Chapter 4. Verification of the land cover classification model 30 4.1. Study area and data acquisition 31 4.1.1. Training area 31 4.1.2. Verification area 32 4.1.3. Data acquisition 33 4.2. Training the land cover classification model 36 4.3. Verification method 37 4.3.1. The performance measurement methods of land cover classification model 37 4.3.2. Accuracy estimation methods of agricultural parcel boundary 39 4.3.3. Comparison of boundary based classification result with ERDAS Imagine 41 4.4. Verification of land cover classification model 42 4.4.1. Performance of land cover classification at the child subcategory 42 4.4.2. Classification accuracy of the aggregated land cover to main category 46 4.4.3. Classification accuracy of boundary based aggregation in agricultural area 57 Chapter 5. Conclusions 71 Reference 73 국 문 초 록 83Maste

Computational simulation of urban expansion using fuzzy cellular automata

170 σ.Είναι εξαιρετικά σημαντικό, η αστική επέκταση να λαμβάνει χώρα με έναν σχεδιασμένο τρόπο, μεγιστοποιώντας τα οφέλη για τον αστικό πληθυσμό, ελαχιστοποιώντας παράλληλα τόσο τα οικονομικά όσο και τα περιβαλλοντικά κόστη. Για να γίνει αυτό χρειάζονται ακριβείς και ρεαλιστικές εκτιμήσεις της αστικής επέκτασης και ευσταθή μοντέλα προσομοίωσης. Η μοντελοποίηση αναφέρεται στην δημιουργία ενός αυστηρά ορισμένου αναλόγου της πραγματικότητας δια μέσου της αφαιρετικής διαδικασίας για να παρέχει προσομοιώσεις και προβολές στο μέλλον υπό συγκεκριμένες θεωρήσεις και να δώσει μια εκτίμηση του πώς μπορεί να μοιάζει το μέλλον. Δεν υπάρχει ωστόσο κάποιο σαφές πλαίσιο για την μοντελοποίηση ενός σύνθετου χώρο-χρονικού φαινομένου όπως η αστική επέκταση, καθώς υπάρχει σημαντική ενδογενής χωρική και χρονική ετερογένεια, αλλά και ετερογένεια σε επίπεδο λήψης αποφάσεων. Παράλληλα, οι γνώσεις μας φαίνεται ότι δεν περιγράφουν την δυναμική της αστικής επέκτασης στο σύνολο της, αλλά μόνο το τμήμα της που έχει παρατηρηθεί και καταγραφεί. Επιπλέον, η γνώση μας για τις διαφορετικές λειτουργικές κλίμακες του αστικού τύπου, των αστικών διαδικασιών και την αλληλεπίδραση των διαφόρων κλιμάκων, είναι ελλιπής, ανάμεσα στα άλλα, εξαιτίας της έλλειψης λεπτομερών χωρικών δεδομένων. Συν τοις άλλοις, προκειμένου ένα μοντέλο να είναι χρήσιμο, πρέπει όχι μόνο να παρέχει ακριβείς εκτιμήσεις αλλά και να περιγράφει τα αποτελέσματα και τους μηχανισμούς του – τις σχέσεις, τις αλληλεπιδράσεις και τις θεωρήσεις – με έναν ανοιχτό, εμφανή και κατανοητό τρόπο έτσι ώστε να μπορούν να αμφισβητηθούν. Για τους παραπάνω λόγους εξελίχθηκε ένα πλαίσιο μοντελοποίησης της αστικής επέκτασης με το κωδικό όνομα CaFe (Cellular Automata Fuzzy Engine), ο στόχος του οποίου είναι: • Να είναι αξιόπιστο και ρεαλιστικό. Δηλαδή να παρέχει ακριβείς εκτιμήσεις για την μελλοντική αστική κάλυψη και να αναπαράγει αποδοτικά τα υποκείμενα χώρο-χρονικά πρότυπα της δυναμικής της αστικής επέκτασης. • Να είναι ανοιχτό και προσαρμόσιμο στις επιθυμίες του χρήστη. Αυτό περιλαμβάνει: o να περιγράφει τους μηχανισμούς του μοντέλου και την ροή της πληροφορίας με απλό και κατανοητό τρόπο και o να υποστηρίζει την προσομοίωση εναλλακτικών σεναρίων αστικής επέκτασης. • Να είναι εύχρηστο και ελαστικό απέναντι στους περιορισμούς της μελέτης, δηλαδή: o να διατηρεί μια προσαρμόσιμη μορφή ώστε να μην υπόκειται σε σημαντικούς περιορισμούς δεδομένων και o να χρησιμοποίει μια μορφή γνώσης, τέτοια ώστε να μπορούν να προστεθούν ή να αφαιρεθούν κανόνες χωρίς να επηρεάζεται ο πυρήνας της γνώσης. Το προτεινόμενο μοντέλο αναπαριστά την γνώση σε μορφή κανόνων οι οποίοι μπορεί να είναι εμπειρικοί ή εξαγόμενοι εκ των δεδομένων. Επίσης μπορεί να είναι χώρο-χρονικά μεταβαλλόμενοι. Ως εκ τούτου η βάση γνώσης (το σύνολο των κανόνων) μπορεί να ταιριάξει καλύτερα στην πραγματικότητα, επιτρέποντας στον χρήστη να υπερβεί πιθανές ελλείψεις δεδομένων εισάγοντας εξωγενή γνώση, προσαρμοσμένη στο μοντέλο διαμέσου εμπειρικών προτύπων ομοιότητας. Παράλληλα, εφαρμόζεται μια καινοτόμα σύνδεση μεταξύ των εξωγενών παραμέτρων (των μεταβλητών εισόδου) και του συστήματος προσομοίωσης της αστικής κάλυψης (μεταβλητή εξόδου). Όλες οι μεταβλητές εισόδου συγχωνεύονται σε μια μόνο εσωτερική μεταβλητή, την ‘καταλληλότητα προς αστικοποίηση’, που καθορίζει την δυνατότητα κάθε περιοχής να αναπτυχθεί. Για να γίνει αυτό εξελίχθηκε ο τελεστής ‘ευαίσθητο άθροισμα’, ένας νέος ασαφής τελεστής που εφαρμόζει μια δυναμική παράλληλη συνδεσμολογία μεταξύ των διαφορετικών μεταβλητών, λαμβάνοντας υπόψη τη μεταξύ τους στατιστική συσχέτιση. Σαν αποτέλεσμα δεν απαιτούνται συγκεκριμένες μεταβλητές ενώ υποστηρίζεται μια μορφή βάσης γνώσης που μπορεί εύκολα να επεκταθεί ή να περιορισθεί. Ο μηχανισμός προσομοίωσης διαχωρίζεται από τον υπολογισμό της ‘καταλληλότητας προς αστικοποίηση’ και ενσωματώνει εξελιγμένες τεχνικές κυψελοειδών αυτομάτων. Αυτές εφαρμόζουν χώρο-χρονικές συναρτήσεις μετάβασης με μεταβλητή ακτίνα και υποστηρίζουν ‘δράση από απόσταση’. Ειδικότερα, εκτός από τα ‘κλασσικά’ κυψελοειδή αυτόματα, το μοντέλο εισάγει την χρήση κυψελοειδών αυτομάτων που δρουν κατά κατεύθυνση ενώ υιοθετεί επιπλέον συμπεριφορά ψευδό-πρακτόρων. Το μοντέλο εφαρμόσθηκε για την ευρύτερη περιοχή των Μεσογείων στην ανατολική Αττική με πραγματικά δεδομένα για τα έτη 1988, 2000 και 2007. Ειδικότερα το CaFe δομήθηκε και βαθμονομήθηκε με δεδομένα για την περίοδο 1988-2000 και ακολούθως εφαρμόσθηκε για τις περιόδους 2000-2007 και 1988-2007. Σε αυτές τις περιόδους, η αστική κάλυψη αυξήθηκε κατά 66%, 66% και 200% αντίστοιχα καθιστώντας έτσι την προσομοίωσή της ιδιαίτερα δύσκολη. Και στις τρεις περιόδους/εφαρμογές ωστόσο, το μοντέλο αποτυπώνει με επιτυχία την δυναμική της αστικής επέκτασης ενώ προσομοιώνει την αστική ανάπτυξη με ικανοποιητική ακρίβεια επιτυγχάνοντας υψηλούς δείκτες προσαρμογής αλλά και σχετικά σταθερό μέσο σφάλμα στις διαφορετικές εφαρμογές.It is of major importance that urban growth occurs in a planned way, maximizing the benefits for urban population while minimizing both environmental and economical cost. This requires accurate and realistic estimations of the urbanization process and this is what urban models do. The term modeling refers to creating a strictly defined analog of real world by subtraction and provides simulations and future projections under identifiable assumptions to suggest what the future might be like. Nevertheless, there is no rigorous framework for modeling such a spatio-temporal phenomenon as urban growth since there lies great inherent spatial, temporal and decision-making heterogeneity, which results from socio-economic and historical heterogeneity itself. Apparently, our knowledge, is not really describing urban growth dynamics in general, but instead the part of the urban growth dynamics that have already occurred and have been observed and experienced. What is more, knowledge about the operational scale(s) of urban form and process, and the interaction and parallelism among different scales, is poor, partially due to the recurring problem of lacking spatially detailed data. Apart from dealing with the above mentioned issues, for a model to be useful it should not only provide accurate estimations but also express both its results and its mechanism – relations, interactions and assumptions – in an open, visible, explicit and comprehensible way in order to be challenged by knowledgeable people. For these reasons a modeling framework has been developed, CaFe (Cellular Automata - Fuzzy Engine) whose goal is to: • Be reliable and realistic. This means to provide accurate estimations for the future urban cover and reproduce efficiently the spatio-temporal patterns of the urban growth dynamics • Be open and adaptable to the user’s requirements. This is to: o describe the model’s mechanisms and the workflow in simple and comprehensible terms and o support the population of alternative scenarios • Be easy to use and resilient to the case study limitations, more specifically to: o sustain a generic versatile form, disengaged from severe data limitations and o use knowledge base in such form that it can be reduced or extended The proposed model is rule-based and supports spatio-temporal rules that may be either data-driven or empirical. As a result, the knowledge base may fit better to reality allowing the user to overcome possible data limitations – which lead to lacking of specific knowledge – by using exogenous knowledge adapted to the model according to empirical similarity patterns. What allows the desired objectives to be accomplished is the combination of the descriptive strength of Fuzzy Logic and the computational strength of Cellular Automata. The model applies an innovative workflow to connect the input and the output variables. All input variables are merged in a single thematic layer, the ‘urbanization suitability’, which defines the potentials of each area to develop. To do so, the model utilizes Sensitive Sum; a new fuzzy operator that is developed to employ a dynamic parallel connection between the effects of separate input variables while taking into account their statistical correlation. As a result, the model does not require certain variables/data to run while it implements a reducible/extensible form of Knowledge Base which can include both data-driven and empirical rules. The simulation engine is separated by the suitability calculator and incorporates advanced Cellular Automata techniques. These techniques apply spatio-temporal multi-radius transition functions and support action in distance. Specifically, on top of the ‘traditional’ cellular automata, CaFe introduces directional cellular automata and adopts pseudo-agent behavior. CaFe is applied in the broader area of Mesogia in east Attica (Athens – Greece) using real data for 1988, 2000 και 2007. More specifically, the model was structured and calibrated for 1988-2000 and was applied for 2000-2007 και 1988-2007. During these periods, urban cover grew by 66%, 66% and 200% respectively, composing thus a challenging case study. Nevertheless, CaFe manages to map urban cover successfully and to efficiently simulate urban growth while scoring high fitting indicators and retaining a stable average error.Ελευθέριος A. Μαντέλα