Dan Raducanu

Feature Extraction for Patch-Based Classification of Multispectral Earth Observation Images

Recently, various patch-based approaches have emerged for high and very high resolution multispectral image classification and indexing. This comes as a consequence of the most important particularity of multispectral data: objects are represented using several spectral bands that equally influence the classification process. In this letter, by using a patch-based approach, we are aiming at extracting descriptors that capture both spectral information and structural information. Using both the raw texture data and the high spectral resolution provided by the latest sensors, we propose enhanced image descriptors based on Gabor, spectral histograms, spectral indices, and bag-of-words framework. This approach leads to a scene classification that outperforms the results obtained when employing the initial image features. Experimental results on a WorldView-2 scene and also on a test collection of tiles created using Sentinel 2 data are presented. A detailed assessment of speed and precision was provided in comparison with state-of-the-art techniques. The broad applicability is guaranteed as the performances obtained for the two selected data sets are comparable, facilitating the exploration of previous and newly lunched satellite missions.

A convolutional deep belief network for polarimetric SAR data feature extraction

This paper proposes a custom convolutional deep belief network for polarimetric synthetic aperture radar (PolSAR) data feature extraction. The proposed architecture stands out through the interesting features it shows, starting with the fact that it is adapted to fully polarimetric SAR data. Then, the multilayer approach allows the stepwise discovery of higher-level features. The convolutional approach allows the discovery of local, spatially invariant features and makes the architecture scalable to fully sized PolSAR images. The network is trained in an unsupervised manner, without using labeled data and then it succeeds to extract powerful features from PolSAR patches. This fact is demonstrated by applying supervised and unsupervised classification algorithms on features extracted from patches of a fully polarimetric multi-look F-SAR image over Kaufbeuren airfield, Germany.

New MPEG-7 Scalable Color Descriptor Based on Polar Coordinates for Multispectral Earth Observation Image Analysis

Continuously expanding high-resolution and very high resolution multispectral image collections, provided by remote sensing satellites, require specific methods and techniques for data analysis and understanding. Even though there are several patch-based approaches for image classification and indexing, none of them are integrated within a standard. Having the goal to develop an MPEG-7 compliant descriptor for patch-based multispectral earth observation image classification and indexing, we propose a new feature extraction method able to extract maximum information from all the available spectral bands that Sentinel 2, the last generation of remote sensing satellites, provides. Using the polar coordinate transformation of the reflectance values, we obtain illumination invariant features, which can be used along with the scalable color descriptor present in MPEG-7 standard. Also, our method proves to enhance land cover classification of the areas affected by clouds and their shadows and provide similar classification results compared with the homogeneous texture descriptor (HTD), spectral histogram (SH), concatenated HTD with SH features, spectral indices (SIs), and bag-of-words-based descriptors, such as bag-of-SIs and bag-of-spectral-values on cloud-free areas.

Polarimetric SAR data feature selection using measures of mutual information

Several algorithms for polarimetric synthetic aperture radar (PolSAR) data indexing and classification were proposed in the state of the art literature. In particular, one of them computes powerful, compact feature descriptors composed of the first three logarithmic cumulants of the BiQuaternion Fractional Fourier Transform (BiQFrFT) coefficients of PolSAR patches. Since the BiQFrFT of each patch is computed at three different angles, the algorithms result consists in nine complex-valued features (18 real-valued features) for single polarization images and in nine biquaternion-valued features (72 real-valued features) for fully polarimetric images. In this paper feature selection based on mutual information (MI) is employed to optimally select a subset of features, in order to improve the indexing performances and to minimize the classification error. The improved results are shown on two polarimetric images: a L-band PALSAR image over Danubes Delta, Romania and a C-band RadarSAT2 image over Brâila, Romania.