Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.

A Critical Comparison Among Pansharpening Algorithms

A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

Pansharpening by Convolutional Neural Networks

Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literatures for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state-of-the-art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Eleven methods from different classes (component substitution, multiresolution analysis, hybrid, Bayesian and matrix factorization) are analyzed. These methods are applied to three datasets and their effectiveness and robustness are evaluated with widely used performance indicators. In addition, all the pansharpening techniques considered in this paper have been implemented in a MATLAB toolbox that is made available to the community.

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Hyperspectral Pansharpening: A Review

To find out the individual and combined effect of different herbicides on weed control and grain yield of wheat crop, an experiment was carried out at Cereal Crops Research Institute (CCRI) Pirsabak, Nowshera during Rabi season 2006-07. A total of eight treatments were kept in the experiment consisting of six herbicidal, one hand weeding and a weedy check. The experiment was replicated four times in RCBD. The herbicidal treatments were carnfentrazone ethyl + isoproturon @ 0.016, bromoxynil octanovate + heptanovate ester @ 1.23, fenoxaprop-P-ethyl @ 0.94, clodinafop propargyl @ 0.04, clodinafop propargyl + bromoxynil octanovate + heptanovate ester @ 0.04 +1.23, fenoxaprop-P-ethyl + bromoxynil octanovate + heptanovate ester @ 0.94 + 1.23 kg a.i. ha. Data were recorded on fresh weed biomass (g m), number of tillers plant, number of spikes m, plant height, number of grains spike, 1000 grain weight (g) and grain yield (t ha). Carnfentrazone ethyl + isoproturon resulted in the lowest fresh weed biomass (57 g m) as compared to weedy check (276 g m). However, the best treatment of carnfentrazone ethyl + isoproturon was at par with hand weeding. Similarly, maximum number of tillers (8 plant), number of spikes (365 m), plant height (113 cm), number of grains (52 spike), 1000 grain weight (36g) and grain yield (4.18 t ha) were recorded in carnfentrazone ethyl + isoproturon; however it was at par with the hand weeding treatment in most of the parameters. The herbicide combinations were better than other treatments except carnfentrazone ethyl + isoproturon and hand weeding in most of the parameters. Minimum values of the above mentioned parameters were recorded in the weedy check plots. In the light of our data the herbicide carnfentrazone ethyl + isoproturon @ 0.016 kg a.i ha is recommended as post emergence herbicide for wheat crop.

Effect of individual and tank mixed herbicides on yield of wheat crop.

In this paper we propose an efficient method for population density estimation using textons and k nearest neighbor classifier (k-NN). Leung Malik (LM) filter bank is used for texture extraction (textons) from Google Earth Satellite Images and classification into high, medium, low population density and non-populated areas. We have tested the proposed method for 5 different images of cities of Pakistan at high resolution. Comparison of our results with those obtained using Grey Level Co-occurrence Matrix (GLCM) are also presented, indicating the effectiveness of the proposed method.

/pdf/population-density-estimation-using-textons-3oqdrag0we.pdf

Population density estimation using textons

This paper presents the design of a low cost and portable EMG circuit for signal acquisition and signal processing of surface EMG along with motor control and drive circuitry The proposed circuitry was successfully tested on a prosthetic arm The paper discusses various approaches that were tested and highlights the best possible approach The highlighted approach is best for acquiring a high fidelity EMG signal, increase its SNR and process the signal into a form best suited for motor control via a microcontroller The paper further discusses the use of various types of surface electrodes and the variety of components that can be used in the circuitry depending upon their availability in the local market and their economic aspect

Design of low cost and portable EMG circuitry for use in active prosthesis applications

In this paper, we explore the use of textons for image texture classification in the context of population density estimation. For this purpose, we have taken high resolution Google Earth images and classified them into four classes i.e. high population density, medium population density, low population density and unpopulated (land/vegetation) areas. A texton dictionary is first built by clustering the responses obtained after convolving the images with a set of filters i.e. “Filter banks”. Using this dictionary, texton histograms are calculated for each class's texture. These histograms are used as training models. Classification of a test image proceeds by mapping this image to a texton histogram and comparing this histogram to the learnt models. To obtain a quantitative assessment of the efficiency of the proposed method, we compare the results of the proposed method with those obtained through supervised classification based on texture extracted by Gray Level Co-occurrence Matrix (GLCM). The results demonstrate that texton based classification achieves better results.

Image texture classification using textons

Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. Different pansharpening methods produce images with different characteristics. In the 2006 IEEE Data Fusion Contest, A -trous Wavelet Transform based pansharpening (AWLP) and Context Adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, it was observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to develop a method which could produce results locally approximately similar to the best method, among the two pansharpening methods. In this paper we propose a method which selects either of the two methods for performing pansharpening on local regions, based upon the size of the objects. The results obtained demonstrate that the proposed method produces images with quantitative results approximately similar to the method which is better among the AWLP and CBD pansharpening methods.

/pdf/pansharpening-with-a-decision-fusion-based-on-the-local-size-5ce0bzqr62.pdf

Muhammad Murtaza Khan

Papers

Effect of individual and tank mixed herbicides on yield of wheat crop.

Population density estimation using textons

Design of low cost and portable EMG circuitry for use in active prosthesis applications

Image texture classification using textons

Pansharpening with a decision fusion based on the local size information