Minh-Tan Pham

Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification

This paper introduces an extension of morphological attribute profiles (APs) by extracting their local features. The so-called local feature-based APs (LFAPs) are expected to provide a better characterization of each APs’ filtered pixel (i.e., APs’ sample) within its neighborhood, and hence better deal with local texture information from the image content. In this paper, LFAPs are constructed by extracting some simple first-order statistical features of the local patch around each APs’ sample such as mean, standard deviation, and range. Then, the final feature vector characterizing each image pixel is formed by combining all local features extracted from APs of that pixel. In addition, since the self-dual APs (SDAPs) have been proved to outperform the APs in recent years, a similar process will be applied to form the local feature-based SDAPs (LFSDAPs). In order to evaluate the effectiveness of LFAPs and LFSDAPs, supervised classification using both the random forest and the support vector machine classifiers is performed on the very high resolution Reykjavik image as well as the hyperspectral Pavia University data. Experimental results show that LFAPs (respectively, LFSDAPs) can considerably improve the classification accuracy of the standard APs (respectively, SDAPs) and the recently proposed histogram-based APs.

Quasi-Flat Zones for Angular Data Simplification

Quasi-flat zones are based on the constrained connectivity paradigm and they have proved to be effective tools in the context of image simplification and super-pixel creation. When stacked, they form successive levels of the \(\alpha \)- or \(\omega \)-tree powerful representations. In this paper we elaborate on their extension to angular data, whose periodicity prevents the direct application of grayscale quasi-flat zone definitions. Specifically we study two approaches in this regard, respectively based on reference angles and angular distance computations. The proposed methods are tested both qualitatively and quantitatively on a variety of angular data, such as hue images, texture orientation fields and optical flow images. The results indicate that quasi-flat zones constitute an effective means of simplifying angular data, and support future work on angular tree-based representations.

Feature Profiles from Attribute Filtering for Classification of Remote Sensing Images

This paper proposes a novel extension of morphological attribute profiles (APs) for classification of remote sensing data. In standard AP-based approaches, an input image is characterized by a set of filtered images achieved from the sequential application of attribute filters based on the image tree representation. Hence, only pixel values (i.e. gray levels) are employed to form the output profiles. In this paper, during the attribute filtering process, instead of outputting the gray levels, we propose to extract both statistical and geometrical features from the connected components (w.r.t. tree nodes) to build the so-called feature profiles (FPs). These features are expected to better characterize the object or region encoded by each connected component. They are then exploited to classify remote sensing images. To evaluate the effectiveness of the proposed approach, supervised classification using the random forest classifier is conducted on the panchromatic Reykjavik image as well as the hyperspectral Pavia University data. Experimental results show the FPs provide a competitive performance compared against standard APs and thus constitute a promising alternative.

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

A novel efficient method for content-based image retrieval (CBIR) is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints) within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds) formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED) is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

SAR image texture tracking using a pointwise graph-based model for glacier displacement measurement

This paper investigates the problem of glacier flow estimation using Synthetic Aperture Radar (SAR) image data. Our motivation is to exploit a weighted graph model constructed from characteristic points (i.e. keypoints) to measure the displacement vectors located at their positions. In fact, characteristic points are capable of capturing the images radiometric and contextual information. Then, by encoding their interaction and inter-connection, a graph model is able to characterize both intensity and geometry information from the image content, which is relevant for texture tracking task. In this work, we employ a graph-based similarity measure to track the local texture information around each keypoint in order to figure out its correspondence from the other image and calculate the associated displacement. The proposed approach is tested and evaluated using high resolution TerraSAR-X images acquired from the Argentiere Glacier located in the French Alps. Our preliminary experimental results show the algorithms capacity to provide a fast and reliable estimation of glacier flows, especially over highly textured and structured regions.