The senior research project that was completed was a study in the field of remote sensing and research area of image fusion technique development. Image fusion is sometimes referenced as image merging in the literature on the subject. The image fusion techniques that were developed for the project were implemented using digital image processing methods. An image fusion algorithm with two main p...

A semantic segmentation algorithm must assign a label to every pixel in an image. Recently, semantic segmentation of RGB imagery has advanced significantly due to deep learning. Because creating datasets for semantic segmentation is laborious, these datasets tend to be significantly smaller than object recognition datasets. This makes it difficult to directly train a deep neural network for sem...

Cloud is one of the most common interferers in Moderate Resolution Imaging Spectrum-radiometer (MODIS) remote sensing imagery. Because of cloud interference, much important and useful information covered by cloud cannot be recovered well. How to detect and remove cloud from MODIS imagery is an important issue for wide application of remote sensing data. In general, cloud can be roughly divided ...

QuickBird satellite imagery, provided by DigitalGlobe Inc., has the highest resolution, among the satellite imaging systems that are commercially available. The QuickBird imaging system simultaneously collects 67-72 centimeter resolution stereo panchromatic and 2.44-2.88 meter resolution multispectral images. In this research, a QuickBird stereo pair of basic imagery product that was taken over...

This paper describes a method to improve a given segmentation result in order to produce a new, refined and more accurate segmented image. The method consists of three phases: shrinking of the input partitions, filtering of the input imagery leading to a mask image, and expansion of the shrunk partitions within the filtered image. The concept is illustrated for the enhancement of a land cover d...

A recently proposed method for automatic radiometric normalization of multiand hyperspectral imagery based on the invariance property of the Multivariate Alteration Detection (MAD) transformation and orthogonal linear regression is extended by using an iterative re-weighting scheme involving no-change probabilities. The procedure is first investigated with partly artificial data and then applie...

Subpixel detection and classification are important in identification and quantification of multicomponent mixtures in remotely sensed data, such as multispectral/hyperspectral images. A recently proposed orthogonal subspace projection (OSP) has shown some success in Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. Ho...

In this paper we present a new approach in the compression of multispectral images. It is based on the merging of two main tendencies such as the use of KLT as a spectral decorrelator and object based image coding schemes. The use of the principal component in multispectral imagery is described and used to perform a multispectral segmenta-tion. This segmentation is taken as the basis for a spec...

Fully Constrained Least Squares (FCLS) has been widely used and proven to be a powerful tool for hyperspectral image classification. But for multispectral remote sensing images with only a few bands, the Least-Squares based approaches will all encounter the band number constraint (BNC), which requires the number of bands should be no less than the number of classes. In this paper, we proposed a...