Land cover changes over time due to natural and manmade phenomena. This paper evaluates different methods for change detection using multispectral and hyperspectral images. Change detection methods such as Euclidean distance and differentiation are applied to Landsat 7 and 8 multispectral images to detect changes over a tropical dry forest area. A change detection method that uses Tasseled Cap Transformation (TCT) and thresholding, is applied to both multispectral and hyperspectral images. Results show more changes over large periods of time such as 10 years. TCT detects changes in both multispectral and hyperspectral images. The level and detail of changes that can be obtained depends on the spatial, spectral and temporal resolution of the images.

We consider a general framework for a human-computer interface whereby the human's knowledge is represented as a point in Euclidean space, the intention of the human is signaled to the computer over a noisy channel, and the computer queries the human in a manner that is amenable to human operation. With these constraints at hand, we demonstrate a class of systems that are nonetheless information-theoretically optimal in that the computer very rapidly hones in on the intent of the human. Much recent work on feedback information theory has been dedicated to the exploration of methods by which optimal feedback may be derived for the purpose of expediting the communication of a message point between an inanimate encoder and decoder. Our framework not only takes advantage of previous work to demonstrate its communication optimality from this perspective as well as from an information-theoretic perspective but also contributes two distinct advantages. First, our framework provides a simplified method based on optimal transport theory to generate optimal feedback signals between the computer and human in high dimension, while still preserving communication optimality. Second, our framework specifically lends itself to the integration of a human user by attempting to moderate the difficulty of the task presented to the user, while still preserving optimality. We demonstrate applications of our framework within the context of multi-agent brain-computer interfaces.

Target detection in hyperspectral images (HSIs) is an active area of research; it seeks to detect objects that are small in both number and size within a scene. The proposed work presents a new methodology for target detection in HSIs by combining kurtosis, level sets, and a size-based thresholding strategy. Kurtosis is used as a preprocessing step to initially enhance the targets in an image. Then, level sets identify and mark associations of pixels with similar spectral information as candidate targets. Finally, the size-based thresholding strategy detects true targets and discards false alarms that do not fit with target dimensions set as input parameter. In addition, we propose a novel version of level sets, which is suitable for target detection tasks in HSIs. Results show that the proposed algorithm could successfully detect targets in HSIs, and it gave better performance in terms of the receiver operating characteristic curve than other techniques widely used in target detection such as orthogonal subspace projection, constrained signal detector, constrained energy minimization, adaptive cosine/coherent estimator algorithm, and generalized-likelihood ratio test.

Spatial smoothing over the original hyperspectral data based on wavelet and anisotropic partial differential equations is incorporated using composite kernel in graph-based classifiers. The kernels combine spectral-spatial relationships using the smoothed and original hyperspectral images. Experiments with different real hyperspectral scenarios are presented. Comparison with recent graph-based methods shows that the proposed scheme gives better classification with lower computational cost.

Spatial smoothing over the original hyperspectral data based on wavelet and partial differential equations (PDEs) are incorporated in the classifiers using composite kernel with kNN graphs. The kernels combine spectral-spatial relationships using the smoothed and original images. Experiments with real hyperspectral scenarios are presented. Comparison with recent graph based methods show that the proposed scheme improves existing methods.

A basic problem in computer vision is to develop texture discrimination techniques that perform as well the human visual system. A theoretical framework that relates dynamics of texture discrimination over context changes with perceptual discrimination is presented. The framework consists of a texture space, a perceptual space and a representational space. Texture stimuli are mapped on to the representational space. A nonlinear psychophysical dynamic transformation is used to map texture stimuli input to responses. This dynamics couples the environment to an internal feedback loop that models the non-linearities in sensory and neural processes of psychovisual perception. The framework can be [Coners, R.W., Ng, C.T., 1989] used to perform texture discrimination that preserves perceptual similarity with the performance of the visual system and also used to study how discriminability of texture patterns is affected by changes in a particular texture property over time. Using this framework a procedure to model discrimination of texture is presented. The nonlinear psychophysical dynamics are implemented using simple finite difference scheme and can be easily converted to parallel algorithm.

In this paper, a new algorithm for texture classification based on logical operators is presented. Operators constructed from logical building blocks are convolved with texture images. An optimal set of six operators are selected based on their texture discrimination ability. The responses are then converted to standard deviation matrices computed over a sliding window. Zonal sampling features are computed from these matrices. A feature selection process is applied and the new set of features are used for texture classification. Classification of several natural and synthetic texture images are presented demonstrating the excellent performance of the logical operator method. The computational superiority and classification accuracy of the algorithm is demonstrated by comparison with other popular methods. Experiments with different classifiers and feature normalization are also presented. The Euclidean distance classifier is found to perform best with this algorithm. The algorithm involves only convolutions and simple arithmetic in the various stages which allows faster implementations. The algorithm is applicable to different types of classification problems which is demonstrated by segmentation of remote sensing images, compressed and reconstructed images and industrial images.