Senaras, Caglar
Yuksel, Baris
Ozay, Mete
Yarman-Vural, Fatos
This paper proposes a novel approach to building detection problem in satellite images. The proposed method employs a two layer hierarchical classification mechanism for ensemble learning. After an initial segmentation, each segment is classified by N different classifiers using different features at the first layer. The class membership values of the segments, which are obtained from different base layer classifiers, are ensembled to form a new fusion space, which forms a linearly separable simplex. Then, this simplex is partitioned by a linear classifier at the meta layer. The paper presents the performance results of the proposed model and comparisons with the state of the art classifiers.


A Learning-Based Resegmentation Method for Extraction of Buildings in Satellite Images
Dikmen, Mehmet; Halıcı, Uğur (2014-12-01)
This letter introduces a new method for building extraction in satellite images. The algorithm first identifies the shadow segments on an oversegmented image, and then neighboring shadow segments, which are assumed to be cast by a single building, are merged. Next, candidate regions where buildings most likely occur are detected by using these shadow regions. Along with this information, closeness to shadows in illumination direction and spectral properties of segments are used to classify them as belonging...
Building Detection With Decision Fusion
Senaras, Caglar; Ozay, Mete; Yarman Vural, Fatoş Tunay (Institute of Electrical and Electronics Engineers (IEEE), 2013-6)
A novel decision fusion approach to building detection problem in VHR optical satellite images is proposed. The method combines the detection results of multiple classifiers under a hierarchical architecture, called Fuzzy Stacked Generalization (FSG). After an initial segmentation and pre-processing step, a large variety of color, texture and shape features are extracted from each segment. Then, the segments, represented in different feature spaces are classified by different base-layer classifiers of the F...
Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery
Ok, Ali Ozgun; Senaras, Caglar; Yuksel, Baris (2013-03-01)
This paper introduces a new approach for the automated detection of buildings from monocular very high resolution (VHR) optical satellite images. First, we investigate the shadow evidence to focus on building regions. To do that, we propose a new fuzzy landscape generation approach to model the directional spatial relationship between buildings and their shadows. Once all landscapes are collected, a pruning process is developed to eliminate the landscapes that may occur due to non-building objects. The fina...
Self-supervised building detection with decision fusion
Şenaras, Çağlar; Yarman Vural, Fatoş Tunay; Eren, Pekin Erhan; Department of Information Systems (2013)
This thesis proposes a new building detection framework for monocular satellite images, called Self-Supervised Decision Fusion (SSDF). The model is based on the idea of self-supervision, which aims to generate training data automatically from each individual test image, without any human interaction. This principle allows us to use the advantages of the supervised classifiers in a fully automated framework. The technical shortcomings of the available supervised and unsupervised algorithms, such as difficult...
An automatic geo-spatial object recognition algorithm for high resolution satellite images
Ergul, Mustafa; Alatan, Abdullah Aydın (2013-09-26)
This paper proposes a novel automatic geo-spatial object recognition algorithm for high resolution satellite imaging. The proposed algorithm consists of two main steps; a hypothesis generation step with a local feature-based algorithm and a verification step with a shape-based approach. In the hypothesis generation step, a set of hypothesis for possible object locations is generated, aiming lower missed detections and higher false-positives by using a Bag of Visual Words type approach. In the verification s...
Citation Formats
C. Senaras, B. Yuksel, M. Ozay, and F. Yarman-Vural, “AUTOMATIC BUILDING DETECTION WITH FEATURE SPACE FUSION USING ENSEMBLE LEARNING,” 2012, p. 6713, Accessed: 00, 2020. [Online]. Available: