A new procedure is proposed for agricultural land-use mapping that addresses a known weakness of classical per-pixel methods in situations involving mixed tree crops. The proposed scheme uses a pair of very-high resolution satellite-borne panchromatic and multispectral images and integrates classification results of two parallel and independent analyses, respectively based on spectral and textural information. The multispectral image is divided into spectrally homogeneous but non-contiguous segments using unsupervised classification. In parallel, the panchromatic image is split into a grid of square blocks on which is performed a texture-driven supervised classification. Finally, the spectral and the textural classifications are fused to generate the land-use map. This method contrasts with object-based methods that sequentially perform image segmentation and classification. Results are evaluated both quantitatively and qualitatively, based on field survey ground-truth data. The quantitative assessment is presented in terms of overall accuracy (from 80% to 100% depending on the area) and Kappa coefficients. Visual comparison of the resulting map with the ground-truth is performed, with the analysis of the binary error maps. Merging spectral and textural classifications results in finer border delimitation and improves the overall classification accuracy of agricultural land-use by 27% as compared to textural classification alone.

This paper presents an empirical comparison of two texture descriptors reported in recent publications. The first one is based on discrete Fourier transform (DFT) and the other is based on the Gabor filters. The two are compared for texture recognition and retrieval. To have deeper insight in to the role of neighbourhood properties and the filter banks in the texture description, extensive experimentation is performed over the image sets containing noisy and rotated variants of the textures from Brodatz album. A method for estimating rotation variance (RV) of a texture descriptor is also presented, which gives an idea of how rotation-sensitive is a certain texture descriptor. The results establish that the DFT-based features outperform the features based on Gabor filters in noiseless conditions whereas the later outperform otherwise.

Bloom's taxonomy (BT) is a hierarchical organisation of global educational objectives. Its cognitive domain has been used to improve pedagogy in several disciplines of science, technology, engineering and mathematics (STEM). At the same time, it has helped improve assessment methods in the domain of STEM. The work presented herein describes taxonomic levels specific to learning objectives in the electrical engineering course entitled ‘Signals and Systems' (S&S). Corresponding to those taxonomic levels, the paper also presents test questions to assess the students at different levels of cognition. In order to validate the proposed taxonomy, the presented test paper is used for summative assessment of 981 students. Results of this assessment are found to agree with the assumed taxonomic levels of the test questions. The results of this test fit closely to the bell-curve distribution and are contrasted with the results from an older, stereotypical examination that was not BT-aware.

Providing the ability of classification to computers has remained at the core of the faculty of artificial intelligence. Its application has now made inroads towards nearly every walk of life, spreading over healthcare, education, defence, economics, linguistics, sociology, literature, transportation, agriculture, and industry etc. To our understanding most of the problems faced by us can be formulated as classification problems. Therefore, any novel contribution in this area has a great potential of applications in the real world. This paper proposes a novel way of learning from classification datasets i.e., hierarchical learning through set partitioning. The theory of probabilistic hierarchical learning for classification has been evolved through several works while widening its scope with each instance. The theory demonstrates that the classification of any dataset can be learnt by generating a hierarchy of learnt models each capable of classifying a disjoint subset of the training set. The basic assertion behind the theory is that an accurate classification of complex datasets can be achieved through hierarchical application of low complexity models. In this paper, the theory is redefined and revised based on four mathematical principles namely, principle of successive bifurcation, principle of two-tier discrimination, principle of class membership and the principle of selective data normalization. The algorithmic implementation of each principle is also discussed. The scope of the approach is now further widened to include ten popular real-world datasets in its test base. This approach does not only produce their accurate models but also produced above 95% accuracy on average with regard to the generalising ability, which is competitive with the contemporary literature.

We present a method of introducing rotation invariance in texture features based on a local Fourier histogram (LFH) computed using a 1-D discrete Fourier transform (DFT). To compensate for image rotation, a local image-gradient angle at each image pixel is found from within one of the 1-D DFT coefficients. The rotation invariance is established theoretically, analytically as well as empirically. The rotation-compensated features extracted from the same texture image oriented at different angles exhibit very high cross correlation. Therefore, the proposed texture features are expected to yield very high accuracies for a variety of image data and applications. The improved LFH-based features outperform the earlier version of the features and the features based on Gabor filters in texture recognition on 8560 images from the Brodatz album.