Name
Title | ||
|---|---|---|
Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics |
Abstract | ||
|---|---|---|
In this paper, we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: 1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low-level change information between the time layers and object-level building description to recognize and separate changed and unaltered buildings. 2) To answer the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature-based modules. 3) To simultaneously ensure the convergence, optimality, and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel nonuniform stochastic object birth process which generates relevant objects with higher probability based on low-level image features. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TPAMI.2011.94 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
global optimization,probabilistic method,probability,image features,remote sensing,stochastic processes,change detection,feature extraction,image segmentation,edge detection,computational complexity,image processing,pixel | Computer vision,Change detection,Global optimization,Computer science,Feature (computer vision),Image processing,Stochastic process,Image segmentation,Feature extraction,Probabilistic method,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
34 | 1 | 0162-8828 |
Citations | PageRank | References |
42 | 1.37 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Csaba Benedek | 1 | 193 | 21.31 |
| Xavier Descombes | 2 | 693 | 79.43 |
| Josiane Zerubia | 3 | 2032 | 232.91 |