فصلنامه سنجش از دور و GIS ایران
سال هفتم شماره 3 (پیاپی 27، پاییز 1394)

The mixed pixels are considered as a major challenge in land cover mapping procedure from satellite imagery. Developments of the spectral unmixing and soft classification methods have provided the possibility for estimation of class proportions within the pixels. However, sub-pixel land cover mapping requires the spatial allocation of the sub-pixels. Recently, the Super Resolution Mapping (SRM) techniques have been developed for optimization of the sub-pixels spatial arrangement using the outputs of soft classifiers and based on the concepts of spatial dependency. In this research, the overall capability of the simulated annealing algorithm was evaluated through sub-pixel land cover mapping of the study area. To do so, a novel method was proposed for generating new solutions in each step of the algorithm and then the results were compared to the traditional method. On the other hand, the effective parameters on the performance of the algorithm (e.g. zoom factor, cooling function type, static and dynamic iterations) were investigated. According to the obtained results, higher values of zoom factor yields more promising overall accuracy . Also, the geometric function was found as the optimal cooling function with respect to the overall accuracy and processing speed. Meanwhile, dynamic iterations demonstrated more accuracy than the static case. As another key result of the paper, the proposed method for generating the new solutions in simulated annealing algorithm is led to increasing of the overall accuracy and also reducing the processing time of algorithm up to 50 percent. The most accurate result of the proposed algorithm, which was obtained for the that case of being independent from soft classifier, is determined 94.97 percent.

Earthquake is one of the most destructive natural disasters which frequently occurs with different intensities. Earthquakes cause severe damage to buildings, main roads and most importantly, loss of life. Detection of damaged buildings caused by such an event at the right time is a critical issue for crisis management and disaster relief. The aim of this study is to detect earthquake damaged buildings using very high resolution (VHR) satellite imagery. To achieve this result, the satellite images with very high resolution before and after the earthquake in Port-au-Prince in Haiti as well as the observed destruction map in 2010 were used. In this study, the optimum features extracted from the image were selected using correlation analysis. The buildings destroyed were classified using fuzzy inference system and the values of selected textures. Finally, the damage map obtained from the proposed algorithm was compared to the map of the area. The kappa criterion estimated from the results of the proposed method is 82% while the index- Jaccard parameter is 89.69%.

Nowadays, extraction of roads from digital aerial and satellite images is a common method of road database construction. Regarding to massive amount of road data and time and cost effective updating requirements, automation procedure is becoming an essential. In this research, which is mostly concentrated on road vectorization process, an automatic approach of road centerline vectorization from detected road image with negligible operator interventions is designed. The proposed system consists of two main stages including road key points determination and connection. At the first stage, the road key points representative of the road centerline are determined using particle swarm optimization clustering. At the second stage, in order to model the road networks weighted graph theory is considered. In this model cost of each connection is calculated by aggregating appropriate road geometric criteria by means of ordered weighted averaging operators. The least cost connections constitute the vectorized road networks. The proposed approach was implemented on several high resolution satellite images and their results were compared with the results of the minimum spanning tree algorithm. On the whole, the obtaining results proved the efficiency of the vectorization approach in attaining the complete and accurate road network.
Extracting different road shapes including direct and curved roads, roads with different widths, parallel roads with different distances, junctions and square with average RMSE value about 0.9 meter, average completeness of %94, and average correctness greater than %95 proves the efficiency of the algorithm in yielding complete road networks.

Calculating the canopy temperature and land surface temperatureusing satellite imagery is very attractive to estimate actual evapotranspiration (ET) by energy balance algorithm. In studies, to evaluate ET, the accuracy of the calculated thermal gradient between surface and air, as well as temperature difference between various land covers is very important. To calculate land surface temperature (LST) in Shahr-E-Kord plain, the study area, there were three principal challenges. First, the absence of enough studies about calculating LST using Landsat8 thermal bands, the second, lack of canopy temperature and land surface temperature observed data, and finally, the only available data for surface temperature was minimum daily surface temperature in the climatology and synoptic stations. In this study, in order to convert the surface brightness temperature to the LST, the split-window algorithm of NOAA-AVHRR was used. Also, the proposed SEBAL algorithm was applied to calculate the surface emissivity. Due to the lack of the reference weather stations, after calculating LST at the satellite overpass time in non-reference weather stations, the deviation error calculation method was used to calibrate satellite LST and to prepare daily LST layers. Results showed that all calculated correlation coefficients were more than 0.9. Also, all existing regression relations were significant at 95% and even 99% level of confidence. In different day-images, maximum difference of calculated deviation errors was less than 0.5 K and, the calculated RMSEs were between 1.9 to 2.2 K, acceptable comparing to similar studies.

Most of common classification algorithms in remote sensing are based on spectral characteristics of the pixels. These approaches result in ignorance of many precious information, such as texture, in the classification process. The urban environment has an inhomogeneous texture, which makes land covers detection a complicated process. In this study, use of texture extracted from the panchromatic image of ALI detector for improvement of Hyperion image's classification accuracy in urban regions was analyzed. Classification carried out using Random Forests method and in five different scenarios. These scenarios included: 1- Classification of the fused image by CNT method (Without Incluion of Texture Information), The other four scenarios covered the classification used by simultaneous use of texture extracted by Gray Level Co-occurrence Matrix »GLCM« in 4 different window sizes: 3, 5, 7, 9 and fused image. Results of these analyses revealed that use of texture information as a useful parameter can lead to an enormous improvement in classification accuracy. Our findings showed that use of texture resulted in an increase in overall accuracy by around 10 percent from 80. 47 to 90. 74 percent. Many of land use/land covers such as roads, residential and industrial areas also experienced the improvement in producer and user accuracies. OOB error as an essential random forests parameter inclined as far as 11 percent from 19. 86 to 8. 87 percent. Moreover, the GLCM feature vectors such as mean and contrast achieved high ranks in importance evaluation in random forests classification. Increase of window size also led to a rise of classification accuracy and the window size 9 gained the highest accuracy accordingly.

The change detection using satellite images is one of the main fields of remote sensing researches. Numerous errors in the images are one problem with the use of satellite imagery. Errors caused by surface illumination, are the main problems in the process of change detection. Therefore, in this study, in order to reduce errors in the results of the detection of changes using change vector analysis, a simple, yet effective method to reduce errors caused by topography is proposed. After applying Tasseled Cap Transform on images, the proposed method compare the angle between direction of change vector with the direction of vector of the pixel in the base image using spectral space, and then calculates the angular threshold. The area under the ROC curve, the Probability Detection and False alarm of proposed method are 0.970, 0.97 and 0.32 respectively.

C-means clustering models are one of the most widely used methods for unsupervised classification of any data. Fuzzy c-means (FCM) is one of the most well-known clustering models in which, each data may be belonged to multiple clusters with different membership degree between 0 and 1. This model has been employed for different application including remotely sensed data classification. FCM model uses Euclidean distance for clustering and assumes the same shape/distribution for all of clusters. However, this causes misclassification in data in which the classes have different shape and size. In this paper, Gustafson-Kessel clustering model is presented to overcome this problem. This model is based on using a fuzzy covariance matrix for each cluster which does not consider the same geometric shape, size and orientation for all clusters. The above models were applied for clustering of hyperspectral imagery issue of Hyperion, ROSIS and CASI sensors. The results of Gustafson-Kessel clustering model prove that the accuracy of classification increased about 12.5% for Hyperion imagery and about 8.45% for ROSIS imagery. Also, the visual test on CASI imagery show that Gustafson-Kessel clustering model has better performance.