فهرست مطالب

Journal of Radar and Optical Remote Sensing
Volume:2 Issue: 4, Autumn 2019

  • تاریخ انتشار: 1399/07/08
  • تعداد عناوین: 6
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  • Ali Emadizadeh, Zahra Azizi * Pages 7-20

    Fire is a major factor in the development of some plant communities, especially those exposed to lightning. Lightning is almost the main cause of natural fires in most plant communities. Fire is effective in the evolution of various species of forests, pastures, and shrubs in arid regions of the world's Mediterranean regions. Remote sensing and geographic information systems are appropriate in assessing the severity of burns. In this study, the intensity of the fire in Gorgan forests is evaluated and examined. The period of the study area was from 2013 to 2017 and Landsat 8 satellite imagery was used. First, the fire points were identified within an area of 500 meters by the IDW method. Then, by using NDVI, NBR, and dNBR indicators, fire points were evaluated and fire points were marked with red pixels which is clear in the two pictures before and after the fire. Finally, it was concluded that the NBR and dNBR index are the most accurate indicators with an accuracy of more than 74%.

    Keywords: Forest, Fire, NDVI index, NBR index, dNBR index, Regression
  • Ehsan Izadi, Ali Akbar Jamali * Pages 21-30

    Today, the rapid growth of the world's urban population, especially in developing countries, has created many problems in various fields. Among these, land-use change is of great importance. Modeling and predicting future land-use changes has become increasingly important for urban and environmental management and other relevant authorities and researchers. The main purpose of this study is to apply cellular automata (CA) Markov models based on spatial information system to simulate and predict land-use change. Landsat satellite imagery was prepared during the three periods of late June 1986, 2001, and 2016. Then land use maps of the study area were obtained by classifying the maps. The model derived from the CA Markov was implemented to predict and process and to analyze land-use changes by 2031. Forecast results showed that from 2016 to 2031, green space, urban residential land use increased and the agricultural and open land use declined. This study will generally show the decline in open land and agriculture and the expansion of residential and urban areas in 2031, which was caused by the loss of agricultural land and vegetation. The region's economy, based on agricultural and livestock production will face the current productivity situation in 2031.

    Keywords: forecasting, Trend, Markov, Modeling, Changes
  • Mina Solhi *, Mehran Yazdi, Mahmoud Sharzehei Pages 31-48

    In recent years, various image integration techniques have been developed to improve their quality. In this paper, some image integration techniques such as Intensity-Hue-Saturation (HIS), Brovey transform, feedback, non-feedback retina model, wavelet transform, and curvelet transform are investigated to improve the spectral and spatial information of satellite images. Also, a new algorithm has been proposed to improve the image quality resulting from the combination of SAR and visible-like images. In the proposed method, the curvelet transform is first applied to the three input levels of Synthetic Aperture Radar (SAR) and visible-like images, then using horizontal cells in the feedback retina model, spectral and spatial information below a specified and adjustable frequency is determined by a Gaussian low-pass filter and replaced with the curvelet coefficients of the integrated image approximation sub-band. Moreover, fine1 and detail1 sub-bands are selected from the visible-like image, and the coefficients of fine2, detail2 sub-bands are weighted and aggregated from both SAR and visible-like images in a specific way. Spectral and spatial quality evaluation criteria including Quality Index (Q_I), Measure the Quality of edges (Q^(AB/f)) Relative Dimensionless Global Error in System (ERGAS), Mutual Information (MI), Euclidian Distance (ED)  and Standard Deviation (STD)  were used to compare and analyze the results of the methods. The results of this evaluation indicated the remarkable performance of the proposed method in preserving the spectral and spatial information content of the integrated image compared to other methods.

    Keywords: fusion, SAR image, Optic image, Curvelet Transform, Feedback Retina model
  • Mohammadjavd Nateghi * Pages 49-57

    Tracking targets from the ground is difficult due to natural and artificial barriers, and in some cases, such as car detection, is dangerous, therefore, identifying targets using remote sensing is obvious. To achieve the purpose, the desired camera is installed on the unmanned aerial vehicle (UAV). with images processing on captured images from the camera, the system has used can identify the vehicle using aerial images and follow it if it is necessary. An important issue to this matter is the accuracy of the target detection. Therefore, efficient algorithms should be used in this field, and efforts have been made to use a deep neural network in this regard because it has the best performance rather than other methods. But using this network itself will cause other problems that are especially noticeable in realtime applications of the identification system. Because this type of neural network needs a lot of time to process information. Solving this problem will using strong hardware as much as possible, but these systems cannot be installed on the UAV due to their high weight and large power consumption. For this reason, in this paper, have tried to use pre-processing methods to identify possible moving targets and illuminate other parts of images to reduce the volume of data to make processing easier, and then the system can identify and track the car with the Light MobileNet-SSD network. This method is 25 times faster than other fast methods such as yolov3, and its loss rate is 0.02.

    Keywords: tracking targets, car detection, UAV, deep neural network, remote sensing
  • Mahdi Emambakhsh, Karim Naghdi * Pages 58-65
    The Phenomenon of subsidence of the earth occures causing wide–raging problems and dangers. The occurrence of this phenomenon causes problems for farmers, the destruction of communication lines and infrastructures and some other issues. In this research from differential interference (DINSAR) and using ASAR sensor data from 2009 to 2010 and sensors sentinel 1 in 2014 – 2015 in relation to the Kerman's Orzuiyeh plain, it is tried to depict the phenomenon of desertification in the picture of the Kerman valley. temporal and spatial Changes have been made in this plain. seascape software is used to process image The results of the series analysis the time has come to show that the region is continuously subsiding; the amount of subsidence per year 2008 to 2009 is 15 centimeters, the most abandoned in the northwest and central plain and 2009 year by 9, 100 cm by 2010, which is still the high et amount of subsidence in the northwest and central plain and for the year 2014 to 2015 is 8.2 cm and the largest amount of this phenomenon is in the southeast part of the Orzuiyeh plain. looking at the pictures of the subsidence of these years can be concluded that Orzuiyeh's desertification is dynamic and has a trend from the north the west was southeast of the plain.
    Keywords: Radar interferometer, Orzuiyeh, Subsidence, ASAR, Sentinel 1
  • zeinab Hoseinnejad, Hasan Hasani Moghaddam*, Zahra Parvar, Kourosh Kavousi, Hamid Gashtasb Meigooni Pages 72-90

    fusion of remote sensing data is essential in order to obtain more information from different images. Mapping the vegetation of an area is very important due to its environmental importance. In this research, used Landsat ETM+ images and field surveying to identify vegetation states of the Eshkevarat No hunting zone. After applying necessary preprocessing like gap filling and atmospheric correction, the panchromatic and multi-spectral images were fused based on the FFT-PCA algorithm. In the next section, the fused image was classified based on the Support Vector Machine (SVM), algorithm into five classes. The results showed that the overall accuracy and kappa coefficient of classified images is 0.943% and 0.910 respectively. In order to field surveying of study area, 1-meter plots in 500-meter distance choose and 14 Flora and vegetation species were identified and mapped. The results showed that satellite images have good accuracy in this field but based on its spatial resolution limitations a large number of species present in the area have not been identified. In this research, it is suggested to use a combination of both satellite image sources and field surveys.

    Keywords: FFT-PCA, Flora, vegetation, Landsat ETM+, SVM, Eshkevarat