The automatic recognition of multi-class objects with various backgrounds is a big challenge in the field of remote sensing (RS) image analysis. In this paper, we propose a novel recognition framework for multi-class RS objects based on the discriminative sparse representation. In this framework, the recognition problem is implemented in two stages. In the first, or discriminative dictionary learning stage, considering the characterization of remote sensing objects, the scale-invariant feature transform descriptor is first combined with an improved bag-of-words model for multi-class objects feature extraction and representation. Then, information about each class of training samples is fused into the dictionary learning process; by using the K-singular value decomposition algorithm, a discriminative dictionary can be learned for sparse coding. In the second, or recognition, stage, to improve the computational efficiency, the phase spectrum of a quaternion Fourier transform model is applied to the test image to predict a small set of object candidate locations. Then, a multi-scale sliding window mechanism is utilized to scan the image over those candidate locations to obtain the object candidates (or objects of interest). Subsequently, the sparse coding coefficients of these candidates under the discriminative dictionary are mapped to the discriminative vectors that have a good ability to distinguish different classes of objects. Finally, multi-class object recognition can be accomplished by analyzing these vectors. The experimental results show that the proposed work outperforms a number of state-of-the-art methods for multi-class remote sensing object recognition.

No abstract

No abstract

Despite much success in the application of sparse representation to object tracking, most of the existing sparse-representation-based tracking methods are still not robust enough for challenges such as pose variations, illumination changes, occlusions, and background distractions. In this paper, we propose a robust object-tracking algorithm via local discriminative sparse representation. The key idea in our method is to develop what we believe is a novel local discriminative sparse representation method for object appearance modeling, which can be helpful to overcome issues such as appearance variations and occlusions. Then a robust tracker based on the local discriminative sparse appearance model is proposed to track the object over time. Additionally, an online dictionary update strategy is introduced in our approach for further robustness. Experimental results on challenging sequences demonstrate the effectiveness and robustness of our proposed method.