A Non-Bayesian Model for Tree Crown Extraction using Marked Point Processes

High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable forestry managers to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the resources. Automatic algorithms are needed in that prospect to assist human operators in the exploitation of these data. In this paper, we present a stochastic geometry approach to extract 2D and 3D parameters of the trees, by modelling the stands as some realizations of a marked point process of ellipses or ellipsoids, whose points are the locations of the trees and marks their geometric features. As a result we obtain the number of stems, their position, and their size. This approach yields an energy minimization problem, where the energy embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted, in 2D and 3D. Results are shown on Colour Infrared aerial images provided by the French National Forest Inventory (IFN

RJMCMC point process sampler for single sensor source separation : an application to electric load monitoring

This paper presents an original method to separate the residential electric load into its major components. The method is explained in the particular case of space-heating, which is the most consuming electric end-use in France1. This is a source separation problem from a single mixture. The components to be retrieved are square signals characterized by a periodic regulation and a slowly timevarying duty cycles. A point process is used to model the electric load as a configuration of possibly overlapping square signals, given the priors on magnitude, duty cycle variations and the regulation periodicity. This stochastic process is simulated using a Reversible Jump Markov Chain Monte Carlo procedure. A simulated annealing scheme is used to achieve the posterior density maximization. First results on real data provided by Electricité de France are quite encouraging

A higher-order active contour model of a `gas of circles' and its application to tree crown extraction

Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when the topology of the region is unknown a priori, for example when the entity is composed of an unknown number of similar objects. Higher-order active contours (HOACs) represent one method for the modelling of non-trivial prior knowledge about shape without necessarily constraining region topology, via the inclusion of non-local interactions between region boundary points in the energy defining the model. The case of an unknown number of circular objects arises in a number of domains, e.g. medical, biological, nanotechnological, and remote sensing imagery. Regions composed of an a priori unknown number of circles may be referred to as a `gas of circles'. In this report, we present a HOAC model of a `gas of circles'. In order to guarantee stable circles, we conduct a stability analysis via a functional Taylor expansion of the HOAC energy around a circular shape. This analysis fixes one of the model parameters in terms of the others and constrains the rest. In conjunction with a suitable likelihood energy, we apply the model to the extraction of tree crowns from aerial imagery, and show that the new model outperforms other techniques

Algoritmo semi-automático para el conteo de árboles en plantaciones forestales mediante el uso de imágenes aéreas

La detección de arboles mediante imágenes aéreas es de importancia para la generación de inventarios forestales. Si bien existen varios métodos para cumplir este objetivo, ninguno puede operar en distintos tipos de forestaciones. En este trabajo se presenta un método semi-automático que se basa en el uso de imágenes multi-espectrales de alta resolución en combinación de un modelo que caracteriza la geometría de la copa del árbol y de la sombra que el mismo proyecta. La aplicación de esta metodología sobre imágenes tomadas sobre una plantación de cítricos en la provincia de Salta arroja una exactitud general mayor al 92 %. En cuanto a los errores de detección se observa que los de omisión superan levemente a los de comisión.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Algoritmo semi-automático para el conteo de árboles en plantaciones forestales mediante el uso de imágenes aéreas

La detección de arboles mediante imágenes aéreas es de importancia para la generación de inventarios forestales. Si bien existen varios métodos para cumplir este objetivo, ninguno puede operar en distintos tipos de forestaciones. En este trabajo se presenta un método semi-automático que se basa en el uso de imágenes multi-espectrales de alta resolución en combinación de un modelo que caracteriza la geometría de la copa del árbol y de la sombra que el mismo proyecta. La aplicación de esta metodología sobre imágenes tomadas sobre una plantación de cítricos en la provincia de Salta arroja una exactitud general mayor al 92 %. En cuanto a los errores de detección se observa que los de omisión superan levemente a los de comisión.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Unsupervised Detection of Planetary Craters by a Marked Point Process

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features

Estimation of the Weight Parameter with SAEM for Marked Point Processes Applied to Object Detection

International audienceWe consider the problem of estimating one of the parameters of a marked point process, namely the tradeoff parameter between the data and prior energy terms defining the probability density of the process. In previous work, the Stochastic Expectation-Maximization (SEM) algorithm was used. However, SEM is well known for having bad convergence properties, which might also slow down the estimation time. Therefore, in this work, we consider an alternative to SEM: the Stochastic Approximation EM algorithm, which makes an efficient use of all the data simulated. We compare both approaches on high resolution satellite images where the objective is to detect boats in a harbor.Nous traitons le problème de l'estimation du paramètre d'un processus ponctuel marqué réalisant le compromis entre attache aux données et à priori, dans la définition de la densité de probabilité du processus. Dans des travaux précédants, l'algorithme d'Espérance Maximisation Stochastique (SEM) était utilisé. Cependant, SEM est connu pour avoir de mauvaises propriétés de convergence, ce qui peut également allonger le temps de calcul. C'est pourquoi nous considérons ici une alternative à SEM : l'algorithme EM avec Approximation Stochastique (SAEM), qui fait bon usage de l'ensemble des données simulées. Nous comparons les deux approches sur des images satellitaires de haute résolution où l'objectif est de détecter des bateaux dans des ports

A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction

Forestry management makes great use of statistics concerning the individual trees making up a forest, but the acquisition of this information is expensive. Image processing can potentially both reduce this cost and improve the statistics. The key problem is the delineation of tree crowns in aerial images. The automatic solution of this problem requires considerable prior information to be built into the image and region models. Our previous work has focused on including shape information in the region model; in this paper we examine the image model. The aerial images involved have three bands. We study the statistics of these bands, and construct both multispectral and single band image models. We combine these with a higher-order active contour model of a `gas of circles' in order to include prior shape information about the region occupied by the tree crowns in the image domain. We compare the results produced by these models on real aerial images and conclude that multiple bands improves the quality of the segmentation. The model has many other potential applications, e.g. to nano-technology, microbiology, physics, and medical imaging

Uzaktan Algılama Verilerini Kullanarak Bireysel Ağaç Taç Tespit Algoritması

Teknolojideki büyük gelişmeler, programcıların verileri yönetme yöntemlerini modernize etmelerine olanak sağlamıştır. Bugün yapay zekâ, alt alanlarından birine çok odaklanmış durumda: veri yönetimi. Düzgün çalışması için, AI sistemleri bir tür veri analizi ve yönetimi gerektirir. Ormanlık yerlerden elde edilen veriler araştırmamızda dikkate alınan verilerdir. Yüksekten uçan uçaklar tarafından çekilen fotoğrafları içeren verileri elde ediyor ve işliyoruz. Bu çalışmada Bulgaristan’da Batı Balkan sıra dağlarından ve Bağdat şehrinin farklı bölgelerinden veriler elde edilmiştir. Veritabanı, turunçgiller gibi mevsimlik veya yaprak dökmeyen ağaç türlerinin farklı resimlerini içerir. Görüntü alımına yardımcı olmak için yüksek yoğunluklu, kısa menzilli insansız hava araçları kullanılabilir. Görüntülerin kalitesi, ağaç taçlarını tanıma sorununu bir dereceye kadar hafifletmeye yardımcı olur. Makine öğrenimi konseptine dayalı akıllı bir model oluşturuyoruz ve çeşitli görüntü işleme teknikleri kullanarak orman havadan görüntülerini analiz ediyoruz. Görüntünün kapsamlı bir tanımını sağlamak ve içindeki ağaçları belirlemek mümkündür. Ayrıca, yakalanan ağaçlar sınıflandırılır ve her kategori için veri sağlanır. Kategoriler genç ağaçlar ve yaşlı ağaçlardır. Mükemmel tanıma ve sınıflandırma doğruluğu elde edilir