M. Kakooei; Y. Baleghi
Abstract
Shadow detection provides worthwhile information for remote sensing applications, e.g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus, solar illumination angle is required to find probable shadow areas. In recent years, Very ...
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Shadow detection provides worthwhile information for remote sensing applications, e.g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus, solar illumination angle is required to find probable shadow areas. In recent years, Very High Resolution (VHR) imagery provides more detailed data from objects including shadow areas. In this regard, the motivation of this paper is to propose a reliable feature, Shadow Low Gradient Direction (SLGD), to automatically determine shadow and solar illumination direction in VHR data. The proposed feature is based on inherent spatial feature of fine-resolution shadow areas. Therefore, it can facilitate shadow-based operations, especially when the solar illumination information is not available in remote sensing metadata. Shadow intensity is supposed to be dependent on two factors, including the surface material and sunlight illumination, which is analyzed by directional gradient values in low gradient magnitude areas. This feature considers the sunlight illumination and ignores the material differences. The method is fully implemented on the Google Earth Engine cloud computing platform, and is evaluated on VHR data with 0.3m resolution. Finally, SLGD performance is evaluated in determining shadow direction and compared in refining shadow maps.
M. Kakooei; Y. Baleghi
Abstract
Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling ...
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Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling method is proposed by fusion of optical and normalized DSM data. Spectral and spatial features are fused into a Heterogeneous Feature Map to train the classifier. Evaluation database classes are impervious surface, building, low vegetation, tree, car, and background. The proposed method is implemented on Google Earth Engine. The method consists of several levels. First, Principal Component Analysis is applied to vegetation indexes to find maximum separable color space between vegetation and non-vegetation area. Gray Level Co-occurrence Matrix is computed to provide texture information as spatial features. Several Random Forests are trained with automatically selected train dataset. Several spatial operators follow the classification to refine the result. Leaf-Less-Tree feature is used to solve the underestimation problem in tree detection. Area, major and, minor axis of connected components are used to refine building and car detection. Evaluation shows significant improvement in tree, building, and car accuracy. Overall accuracy and Kappa coefficient are appropriate.
G.3.2. Logical Design
H. Tavakolaee; Gh. Ardeshir; Y. Baleghi
Abstract
Adders, as one of the major components of digital computing systems, have a strong influence on their performance. There are various types of adders, each of which uses a different algorithm to do addition with a certain delay. In addition to low computational delay, minimizing power consumption is also ...
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Adders, as one of the major components of digital computing systems, have a strong influence on their performance. There are various types of adders, each of which uses a different algorithm to do addition with a certain delay. In addition to low computational delay, minimizing power consumption is also a main priority in adder circuit design. In this paper, the proposed adder is divided into several sub-blocks and the circuit of each sub-block is designed based on multiplexers and NOR gates to calculate the output carry or input carry of the next sub-block. This method reduces critical path delay (CPD) and therefore increases the speed of the adder. Simulation and synthesis of the proposed adder is done for cases of 8, 16, 32, and 64 bits and the results are compared with those of other fast adders. Synthesis results show that the proposed 16 and 32-bit adders have the lowest computation delay and also the best power delay product (PDP) among all recent popular adders.
H.5. Image Processing and Computer Vision
Seyed M. Ghazali; Y. Baleghi
Abstract
Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric ...
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Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric situations. In this paper an efficient method is proposed for detecting pedestrians in outdoor thermal images that adapts to variable atmospheric situations. In the first step, the type of atmospheric situation is estimated based on the global features of the thermal image. Then, for each situation, a relevant algorithm is performed for pedestrian detection. To do this, thermal images are divided into three classes of atmospheric situations: a) fine such as sunny weather, b) bad such as rainy and hazy weather, c) hot such as hot summer days where pedestrians are darker than background. Then 2-Dimensional Double Density Dual Tree Discrete Wavelet Transform (2D DD DT DWT) in three levels is acquired from input images and the energy of low frequency coefficients in third level is calculated as the discriminating feature for atmospheric situation identification. Feed-forward neural network (FFNN) classifier is trained by this feature vector to determine the category of atmospheric situation. Finally, a predetermined algorithm that is relevant to the category of atmospheric situation is applied for pedestrian detection. The proposed method in pedestrian detection has high performance so that the accuracy of pedestrian detection in two popular databases is more than 99%.
