Jiangye Yuan

Remote Sensing Image Segmentation by Combining Spectral and Texture Features

We present a new method for remote sensing image segmentation, which utilizes both spectral and texture information. Linear filters are used to provide enhanced spatial patterns. For each pixel location, we compute combined spectral and texture features using local spectral histograms, which concatenate local histograms of all input bands. We regard each feature as a linear combination of several representative features, each of which corresponds to a segment. Segmentation is given by estimating combination weights, which indicate segment ownership of pixels. We present segmentation solutions where representative features are either known or unknown. We also show that feature dimensions can be greatly reduced via subspace projection. The scale issue is investigated, and an algorithm is presented to automatically select proper scales, which does not require segmentation at multiple-scale levels. Experimental results demonstrate the promise of the proposed method.

LEGION-Based Automatic Road Extraction From Satellite Imagery

An automatic method for road extraction from satellite imagery is presented. The core of the proposed method is locally excitatory globally inhibitory oscillator networks (LEGION). The road extraction task is decomposed into three stages. The first stage is image segmentation by LEGION. In the second stage, the medial axis of each segment is computed, and the medial axis points corresponding to narrow regions are selected. The third is the road grouping stage. Alignment-dependent connections between selected points are established, and LEGION is utilized to group well-aligned points, which represent the extracted roads. Due to the selective gating mechanism of LEGION, different roads in an image are grouped separately. Road extraction results on synthetic and real images are presented. A comparison with other methods shows that the proposed method produces very competitive extraction results.

Factorization-Based Texture Segmentation

This paper introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histograms to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. The experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.

Road Segmentation in Aerial Images by Exploiting Road Vector Data

Segmenting road regions from high resolution aerial images is an important yet challenging task due to large variations on road surfaces. This paper presents a simple and effective method that accurately segments road regions with a weak supervision provided by road vector data, which are publicly available. The method is based on the observation that in aerial images road edges tend to have more visible boundaries parallel to road vectors. A factorization-based segmentation algorithm is applied to an image, which accurately localize boundaries for both texture and nontexture regions. We analyze the spatial distribution of boundary pixels with respect to the road vector, and identify the road edge that separates roads from adjacent areas based on the distribution peaks. The proposed method achieves on average 90% recall and 79% precision on large aerial images covering various types of roads.

Systematic Benchmarking of Aerial Image Segmentation

This letter presents a benchmarking study for aerial image segmentation. We construct an image data set consisting of various aerial scenes. Segmentations generated by different human subjects are used as ground truth. We analyze the consistency between segmentations from different subjects. We select six leading segmentation algorithms, which include not only the algorithms specifically designed for aerial images but also more generally applicable algorithms. We also select a recently proposed algorithm due to its promising performance in handling texture regions. We apply these algorithms to the aerial image data set and quantitatively evaluate their performance. We interpret the evaluation results based on the characteristics of algorithms, which provide general guidance for selecting proper algorithms in specific applications.

Automatic road extraction from satellite imagery using LEGION networks

We present an automatic method for road extraction from satellite imagery. The core of the proposed method is Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION). We decompose the road extraction task into three stages. The first stage is image segmentation by LEGION. In the second stage, we compute the medial axis of each segment and select the segments with narrow widths. The third is the road grouping stage. With the medial axes, alignment-dependent connections between medial axis points are established and LEGION is utilized to group the well-aligned medial axes, which represent extracted road segments. Due to the selective gating mechanism of LEGION, different roads in an image are grouped separately. Experimental results on synthetic and real images show the effectiveness of this method.

Image feature based GPS trace filtering for road network generation and road segmentation

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segment road regions. Our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.

Image segmentation using local spectral histograms and linear regression

We present a novel method for segmenting images with texture and nontexture regions. Local spectral histograms are feature vectors consisting of histograms of chosen filter responses, which capture both texture and nontexture information. Based on the observation that the local spectral histogram of a pixel location can be approximated through a linear combination of the representative features weighted by the area coverage of each feature, we formulate the segmentation problem as a multivariate linear regression, where the solution is obtained by least squares estimation. Moreover, we propose an algorithm to automatically identify representative features corresponding to different homogeneous regions, and show that the number of representative features can be determined by examining the effective rank of a feature matrix. We present segmentation results on different types of images, and our comparison with other methods shows that the proposed method gives more accurate results.

Learning to count buildings in diverse aerial scenes

Determining the number of buildings in aerial images is an important problem because the information greatly benefits applications such as population estimation, change detection, and urbanization monitoring. In this paper, we address this problem by learning the relationship between low-level image features and building counts. Building footprints from public cartographic databases are used as labeled data. We first extract straight line segments from images. A classifier is then trained to identify line segments corresponding to building edges. Although there exist mismatches between resulting line segments and building edges, we observe a strong linear relationship between building numbers and line numbers for similar types of buildings. Based on this observation, we predict the building count for a given image using the following method. We find top k images with the most similar appearances from training samples and learn a linear regression model from this image set. The building count is computed based on the model. Our method avoids the difficulty in building detection and produces reliable results on large, diverse datasets.

Image driven GPS trace analysis for road map inference

The trace data generated from GPS enabled vehicles is highly valuable for applications such as map inference and traffic analysis. However, the data tends to be noisy due to signal interference. In this paper, we introduce aerial images in GPS trace analysis. Computer vision techniques are developed that effectively integrate image information with GPS data to generate road networks. An image is first segmented by an efficient factorization-based algorithm. A structure tensor approach is proposed to measure the orientation difference between a trace segment and the corresponding image patch. The segmentation result and orientation measures lead to significantly reducing the traces not aligning with roads. The traces are further processed to produce high-quality road networks. We show that our method produces promising results for very noisy GPS data with a low sampling rate and also outperforms the leading method of map inference from GPS traces.