Spectral images are the most valuable data than can be achieved using 2D sensors. Spectral estimation using data with a few channel cameras has been the subject of many studies. It is common to use color filters in front of the lens for increasing dimensionality of data. However, spectral estimations are prone to suffer from colorimetric errors. To address this problem it was shown that this problem is a special case of error-free spectral estimation problem. Considering the fact that most of RGB cameras tend to be colorimetric, using geometrical modeling of the problem, it was shown that adding a shoot with bare lens to the sensor’ s data can solve the problem. The notion has been tested in different scenarios and the efficiency of the proposed method has been proved in the scenarios. Results showed that if the camera is acceptably colorimetric, the proposed method can even leads to error-free colorimetric performance. Prog. Color Colorants Coat. 13 (2020), 121-130© Institute for Color Science and Technology.

In robotic applications and especially 3D map generation of indoor environments، analyzing RGB-D images have become a key problem. The mapping problem is one of the most important problems in creating autonomous mobile robots. Autonomous mobile robots are used in mine excavation، rescue missions in collapsed buildings and even planets’ exploration. Furthermore، indoor mapping is beneficial in finding and rescuing missions. With recent advances، mobile robots are used in hazardous missions such as radioactive areas or collapsing buildings. Having the environment’ s map beforehand can boost efficiency and effectiveness of the mission. In order to digitize the environment، several 3D scans are needed. However، these scans should be merged according to a global coordination system to create a correct، consistent model. This process is called image registration. If the robot with 3D scanner is able to accurately localize itself، the registration can be done directly by robots pose. However، due to imprecise robot sensors، self-localization is error prone. Therefore، the geometric structure of overlapping 3D scans is considered. In order to registering various points sets، Iterative Closest Point (ICP) algorithm is used. ICP is the most common approach to align point clouds in two consecutive image frames. This algorithm uses a point to point approach. RGB and depth images which are captured by Kinect are used in this study. In order to reducing data points and performing faster 3D map creation، depth images are converted to point clouds and then segmentation is done according to image planes. For this purpose RGB images are segmented by region growing segmentation algorithm. In this algorithm، the image was initially over segmented. This algorithm uses stack data structure and Euclidean distance in Lab color space to segment the image. Euclidean distance in Lab color space describes the resemblance of two colors to each other. In this algorithm، the aim is to label each pixel to a segment. To this end، each unlabeled pixels Euclidean distance to its neighboring mean color is checked to be within a threshold. For over-segmentation، if the distance satisfies the smaller threshold، the more pixels will be merged to the segment. Afterwards a plane was fit to each segment. After segmentation، each segment should be represented by a plane. Eventually، the segments were merged based on the product of normal vectors and plane fitting error criteria. After segmentation، planes were fit to the new segments again. A given number of points were generated on the plane. ICP algorithm was executed on these points and transfer and rotation matrices were obtained. Generating points on the plane results in fewer points. Therefore، the points were reduced and algorithms performance was increased. The results show that the proposed method increases the speed up to 55 and 91 percent in consecutive and non-consecutive frames on average، respectively.

Spectral decomposition of time series has a significant role in seismic data processing and interpretations. Since the earth acts as a low-pass filter, it changes the frequency content of the passing seismic waves. Conventional methods of representing signals in a time domain and frequency domain cannot show the time information and the frequency information simultaneously. Time-frequency transforms an upgraded spectral decomposition to a new step and can show time and frequency information simultaneously.Time-frequency transforms generate a high volume of spectral components, which contain useful information about the reservoir and can be decomposed into single frequency volumes. These single frequency volumes can overload the limited space of a computer hard disk and are not easy for an interpreter to investigate them individually; therefore, it is important to use methods to decrease the volume without losing information. The frequency slices are thus separated from these volumes and used for an interpretation.In this study, three different methods were used to represent a buried channel. In the first method, the numbers of the single frequency slices were investigated, variations of the frequency amplitudes in the slices were observed, and an expert interpreter could obtain some information about the channel content and lateral variation. Since different frequencies contain different types of information (low frequencies are sensible to channel content and high frequencies are sensible to channel boundaries), none of the slices were able to show all information simultaneously. In the next two methods using a color stacking method, the RGB plots were constructed which, due to the different frequency content, resulted in more information than the frequency slice representation method.An RGB image, sometimes referred to as a true color image, is an image that defines red, green, and blue color components for each individual pixel and has an intensity between 0 and 1. In this study, RGB plots were constructed in two different manners, RGB plots based on conventional RGB plot methods and RGB plots using basis functions. In the conventional method, three different frequency slices were mapped against the red, green and blue components. Although this method obviates some drawbacks of the single frequency plots, it uses only three slices and practically ignores a big part of information. Using basis functions and defining windows, the interpreter was able to introduce some frequency intervals and plot them against the primary components and use the total bandwidth or its major part. Three simple raised cosine functions having different frequency centers and different periods were chosen. The image quality strongly depended on these two parameters. Longer window widths will introduce longer frequency widths into every primary component and resulted in smoother color combinations for images and very short periods had the same results as the conventional RGB plot method. Different centers showed different details. Low frequency centers showed channel content properties, and high frequency centers showed channel boundaries and fine branches.In this study, the spectral decomposition was first performed on land seismic data from an oil field in Iran using a short time Fourier (STFT) transform and an S transform. Then three demonstration methods were applied for channel detection. Finally it was shown that how RGB color stacking method represented buried channels in more precise images and how a basis function based RGB represents better results than the conventional RGB method.

CCD Camera is a multi-spectral sensor placed on CBERS02 satellite platform. The imaging technique in this sensor is pushbroom. There are some vertical noises in the images captured by this sensor all because of unadjustment between different adjacent detectors in the CCD-Camera sensor, internal changes in detectors, mis-calibration and low values of signal to noise ratios. These noises for homogeneous surfaces in level2 products are more profound. The presence of these noises in the images renders correct interpretation and extraction of information hard. In this study, for correction of noise a method based on the spatial momentum adjustment is introduced. In the proposed method the statistical momentums such as mean and standard deviation of the column in each band are used for stabilization of the statistical characteristics of the detector array to their reference values. In the simulated image for vertical noise, 97% accuracy in denoising was achieved. Moreover 16% increase of the image quality in band1 and 19% in band2 shows the acceptable performance of the method in denoising. Also by implementing the method on band1 and 2 images, the standard deviation decreased from 9.47 to 9.01 and 5.72 to 5.25 respectively. This proves that the method was a success.