Zdenka Babic

Fusion of Global and Local Descriptors for Remote Sensing Image Classification

Very high resolution remote sensing images offer increased amount of details available for image interpretation. However, despite enhanced resolution, these details result in spectral inhomogeneities, making automated image classification more difficult. In this letter, we propose to combine texture and local image features to address this problem. We first address the enhanced Gabor texture descriptor which is a global descriptor based on cross correlations between subbands and show that it achieves very good results in classification of aerial images showing a single thematic class. Next, the performances obtained on individual land cover/land use classes using our global texture descriptor and local scale-invariant feature transform descriptor are compared. We identify classes of images best suited for each descriptor and argue that these descriptors encode complementary information. Finally, a hierarchical approach for the fusion of global and local descriptors is proposed and evaluated over a number of classifiers. The proposed descriptor fusion approach exhibits significantly improved classification results, reaching the accuracy of around 90%.

An iterative logarithmic multiplier

Digital signal processing algorithms often rely heavily on a large number of multiplications, which is both time and power consuming. However, there are many practical solutions to simplify multiplication, like truncated and logarithmic multipliers. These methods consume less time and power but introduce errors. Nevertheless, they can be used in situations where a shorter time delay is more important than accuracy. In digital signal processing, these conditions are often met, especially in video compression and tracking, where integer arithmetic gives satisfactory results. This paper presents a simple and efficient multiplier with the possibility to achieve an arbitrary accuracy through an iterative procedure, prior to achieving the exact result. The multiplier is based on the same form of number representation as Mitchells algorithm, but it uses different error correction circuits than those proposed by Mitchell. In such a way, the error correction can be done almost in parallel (actually this is achieved through pipelining) with the basic multiplication. The hardware solution involves adders and shifters, so it is not gate and power consuming. The error summary for operands ranging from 8 bits to 16 bits indicates a very low relative error percentage with two iterations only. For the hardware implementation assessment, the proposed multiplier is implemented on the Spartan 3 FPGA chip. For 16-bit operands, the time delay estimation indicates that a multiplier with two iterations can work with a clock cycle more than 150MHz, and with the maximum relative error being less than 2%.

Gabor descriptors for aerial image classification

The amount of remote sensed imagery that has become available by far surpasses the possibility of manual analysis. One of the most important tasks in the analysis of remote sensed images is land use classification. This task can be recast as semantic classification of remote sensed images. In this paper we evaluate classifiers for semantic classification of aerial images. The evaluated classifiers are based on Gabor and Gist descriptors which have been long established in image classification tasks. We use support vector machines and propose a kernel well suited for using with Gabor descriptors. These simple classifiers achieve correct classification rate of about 90% on two datasets. From these results follows that, in aerial image classification, simple classifiers give results comparable to more complex approaches, and the pursuit for more advanced solutions should continue having this in mind.

Aerial image classification using structural texture similarity

There is an increasing need for algorithms for automatic analysis of remote sensing images and in this paper we address the problem of semantic classification of aerial images. For the task at hand we propose and evaluate local structural texture descriptor and similarity measure. Nearest neighbor classifier based on the proposed descriptor and similarity measure, as well as image-to-class similarity, improves classification rates over the state-of-the-art on two datasets of aerial images. We evaluate the design choices and show that rich subband statistics, perceptually-based structural texture similarity measure and image-to-class similarity all contribute to the good performance of our classifier.

Peer-to-Peer Communication in Neuronal Nano-Network

Serving as peers in the central nervous system, neurons make use of two communication paradigms, electrochemical, and molecular. Owing to their effective coordination of all the voluntary and involuntary actions of the body, an intriguing neuronal communication nominates as a potential paradigm for nano-networking. In this paper, we propose an alternative representation of the neuron-to-neuron communication process, which should offer a complementary insight into the electrochemical signals propagation. To this end, we apply communication-engineering tools and abstractions, represent information about chemical and ionic behavior with signals, and observe biological systems as input-output systems characterized by a frequency response. In particular, we inspect the neuron-to-neuron communication through the concepts of electrochemical communication, which we refer to as the intra-neuronal communication due to the pulse transmission within the cell, and molecular synaptic transmission, which we refer to as the inter-neuronal communication due to particle transmission between the cells. The inter-neuronal communication is explored by means of the transmitter, the channel, and the receiver, aiming to characterize the spiking propagation between neurons. Reported numerical results illustrate the contribution of each stage along the neuronal communication pathway, and should be useful for the design of a new communication technique for nano-networks and intrabody communications.

Unsupervised Quaternion Feature Learning for Remote Sensing Image Classification

Bag-of-words image representations based on local descriptors are common in image classification and retrieval tasks. However, in order to achieve state-of-the-art results, complex hand-crafted feature filters and/or support vector classifiers with nonlinear kernels are needed. Compared with hand-crafted features, unsupervised feature learning is a popular alternative, which results in feature filters adapted to the problem domain at hand. Although both color and intensity are important cues for remote sensing image classification and color images are commonly used for unsupervised feature learning, most of the existing algorithms do not take into account interrelationships between intensity and color information. We address this problem using quaternion representation for color images and propose unsupervised learning of quaternion feature filters, as well as feature encoding using quaternion orthogonal matching pursuit (Q-OMP). By using quaternion representation, we are able to jointly encode intensity and color information in an image. We obtain local descriptors by soft thresholding and computing absolute values of scalar and three vector parts of the quaternion-valued sparse code. Local descriptors are pooled, power-law transformed, and normalized, yielding the resulting image representation. The experimental results on UC Merced Land Use and Brazilian Coffee Scenes datasets are comparable or better than the state of the art, demonstrating the effectiveness of the proposed approach. The proposed method for quaternion feature learning is able to adapt to the characteristics of the available data, and being fully unsupervised, it emerges as a viable alternative to both hand-crafted representations and convolutional neural networks, especially in application scenarios with scarce-labeled training data.

A fast algorithm for linear convolution of discrete time signals

A new, computationally efficient, algorithm for linear convolution is proposed. This algorithm uses an N point instead of the usual 2N-1 point circular convolution to produce a linear convolution of two N point discrete time sequences. To achieve this, a scaling factor is introduced which enables the extraction of the term representing linear convolution from any algorithm that computes circular convolution. The proposed algorithm is perfectly accurate provided that the chosen circular convolution algorithm does not introduce round-off errors. The analysis is supported by simulation examples for several typical application cases.

Unsupervised learning of quaternion features for image classification

Unsupervised feature learning is a very popular trend in image classification. Most of the methods for unsupervised feature learning produces filters which operate either on intensity or color information. In this paper we propose a quaternion-based approach for unsupervised feature learning which makes possible joint encoding of the intensity and color information. The image representation is computed using quaternion principal component analysis and k-means clustering. We experimentally show that our approach outperforms the existing approach for unsupervised feature learning from color images, achieving classification accuracy of 91% on a dataset of remote sensing images.

A simple pipelined logarithmic multiplier

Digital signal processing algorithms often rely heavily on a large number of multiplications, which is both time and power consuming. However, there are many practical solutions to simplify multiplication, like truncated and logarithmic multipliers. These methods consume less time and power but introduce errors. Nevertheless, they can be used in situations where a shorter time delay is more important than accuracy. In digital signal processing, these conditions are often met, especially in video compression and tracking, where integer arithmetic gives satisfactory results. This paper presents and compare different multipliers in a logarithmic number system. For the hardware implementation assessment, the multipliers are implemented on the Spartan 3 FPGA chip and are compared against speed, resources required for implementation, power consumption and error rate. We also propose a simple and efficient logarithmic multiplier with the possibility to achieve an arbitrary accuracy through an iterative procedure. In such a way, the error correction can be done almost in parallel (actually this is achieved through pipelining) with the basic multiplication. The hardware solution involves adders and shifters, so it is not gate and power consuming. The error of proposed multiplier for operands ranging from 8 bits to 16 bits indicates a very low relative error percentage.

A GPU implementation of a structural-similarity-based aerial-image classification

There is an increasing need for fast and efficient algorithms for the automatic analysis of remote-sensing images. In this paper we address the implementation of the semantic classification of aerial images with general-purpose graphics-processing units (GPGPUs). We propose the calculation of a local Gabor-based structural texture descriptor and a structural texture similarity metric combined with a nearest-neighbor classifier and image-to-class similarity on CUDA supported graphics-processing units. We first present the algorithm and then describe the GPU implementation and optimization with the CUDA programming model. We then evaluate the results of the algorithm on a dataset of aerial images and present the execution times for the sequential and parallel implementations of the whole algorithm as well as measurements only for the selected steps of the algorithm. We show that the algorithms for the image classification can be effectively implemented on the GPUs. In our case, the presented algorithm is around 39 times faster on the Tesla C1060 unit than on the Core i5 650 CPU, while keeping the same success rate of classification.