In view of the long and narrow characteristics of the airport runway function area, this paper proposes a method combining the Haar -like feature and the spherical neural network to detect the airport runway in remote...
详细信息
The thirteen papers in this special section were presented at the 9th International Workshop on the Analysis of Multitemporal remotesensingimages (MultiTemp 2017), hosted by VITO remotesensing on June 27-29, 2017.
The thirteen papers in this special section were presented at the 9th International Workshop on the Analysis of Multitemporal remotesensingimages (MultiTemp 2017), hosted by VITO remotesensing on June 27-29, 2017.
We identify a challenging problem of nondominant object recognition with applications in medical/remotesensingimages, and investigate convolutional Neural Network (CNN)'s learning capability towards solving this...
详细信息
Deep Learning is a superb way to solve remotesensing related problems, which mainly cover four perspectives: imageprocessing, pixel-based classification, target recognition and scene understanding. In this paper, we...
详细信息
ISBN:
(纸本)9781538650530
Deep Learning is a superb way to solve remotesensing related problems, which mainly cover four perspectives: imageprocessing, pixel-based classification, target recognition and scene understanding. In this paper, we focus on target recognition by building deep learning models, and our target is sequence pattern. Accurate prediction of sequence pattern would help identify significant characters from text sequence. Despite considerable advances in using machine learning techniques for sequence patternrecognition problem, its efficiency is still limited because of its involving extensive manual feature engineering in the process of features extraction from raw sequences. Thus, we apply a deep learning approach in sequence patternrecognition problem. The sequences of the datasets we used are self-generated genomic format sequences, and each dataset is generated based on a kind of pattern. We then investigate and construct various deep neural network models (such as convolutional networks, recurrent networks and a hybrid of convolutional and recurrent networks). The one-hot encoding method that preserves the vital position information of each character is presented to represent sequences as inputs to the models. The sequence patterns are then extracted from the input and output the probabilities of the existence of sequence patterns. Experimental results demonstrate that the deep learning approaches can achieve high accuracy and high precision in sequence patternrecognition. In addition, a saliency-map-based method is applied to visualize the learned sequence patterns. In view of the simulation results, we believe that we can find an appropriate deep learning model for a certain sequence sensing problem.
Edge-Directed Radial Basis Functions (EDRBF) are used to compute super resolution(SR) image from a given set of low resolution (LR) images differing in subpixel shifts. The algorithm is tested on remotesensingimages...
详细信息
ISBN:
(数字)9781510618046
ISBN:
(纸本)9781510618046
Edge-Directed Radial Basis Functions (EDRBF) are used to compute super resolution(SR) image from a given set of low resolution (LR) images differing in subpixel shifts. The algorithm is tested on remotesensingimages and compared for accuracy with other well-known algorithms such as Iterative Back Projection (IBP), Maximum Likelihood (ML) algorithm, interpolation of scattered points using Nearest Neighbor (NN) and Inversed Distance Weighted (IDW) interpolation, and Radial Basis Functin(RBF). The accuracy of SR depends on various factors besides the algorithm (i) number of subpixel shifted LR images (ii) accuracy with which the LR shifts are estimated by registration algorithms (iii) and the targeted spatial resolution of SR. In our studies, the accuracy of EDRBF is compared with other algorithms keeping these factors constant. The algorithm has two steps: i) registration of low resolution images and (ii) estimating the pixels in High Resolution (HR) grid using EDRBF. Experiments are conducted by simulating LR images from a input HR image with different sub-pixel shifts. The reconstructed SR image is compared with input HR image to measure the accuracy of the algorithm using sum of squared errors (SSE). The algorithm has outperformed all of the algorithms mentioned above. The algorithm is robust and is not overly sensitive to the registration inaccuracies.
In this work, remotesensing reflectance (Rrs) spectra of the Zhejiang coastal water in the East China Sea (ECS) were simulated by using the Hydrolight software with field data as input parameters. The seawater along ...
详细信息
ISBN:
(数字)9781510617186
ISBN:
(纸本)9781510617186;9781510617179
In this work, remotesensing reflectance (Rrs) spectra of the Zhejiang coastal water in the East China Sea (ECS) were simulated by using the Hydrolight software with field data as input parameters. The seawater along the Zhejiang coast is typical Case ii water with complex optical properties. A field observation was conducted in the Zhejiang coastal region in late May of 2016, and the concentration of ocean color constituents (pigment, SPM and CDOM), IOPs (absorption and backscattering coefficients) and Rrs were measured at 24 stations of 3 sections covering the turbid to clear inshore coastal waters. Referring to these ocean color field data, an ocean color model suitable for the Zhejiang coastal water was setup and applied in the Hydrolight. A set of 11 remotesensing reflectance spectra above water surface were modeled and calculated. Then, the simulated spectra were compared with the filed measurements. Finally, the spectral shape and characteristics of the remotesensing reflectance spectra were analyzed and discussed.
Rapid monitoring of vegetation cover with precision has always been a challenge for maintaining accuracy over a large area. remotesensing (RS) based satellite imagery has significantly contributed in monitoring veget...
详细信息
The technological evolution of remotesensing sensors led to the acquisition of huge archives of data that are difficult to interpret by human experts. In order to process this huge amount of data and the large number...
详细信息
ISBN:
(纸本)9781538646953
The technological evolution of remotesensing sensors led to the acquisition of huge archives of data that are difficult to interpret by human experts. In order to process this huge amount of data and the large number of potential temporal evolutions, multitemporal analysis techniques need to be developed. In this paper, we propose a simple, unsupervised, yet effective technique towards the retrieval of spatio-temporal patterns from satellite image time series (SITS). Following a query-by-example procedure, the proposed method is able to extract patterns that are similar to a given query under two use case scenarios, i.e., short and long SITS, respectively. The experiments prove the successful application of the proposed method in both cases.
Motion detection and estimation is an important task in several applications of image analysis, including scenarios such as satellite cross-cueing or detecting small shifts in terrain. One widely employed technique fo...
详细信息
ISBN:
(数字)9781510618107
ISBN:
(纸本)9781510618107
Motion detection and estimation is an important task in several applications of image analysis, including scenarios such as satellite cross-cueing or detecting small shifts in terrain. One widely employed technique for estimating the amount of motion between two images is Normalized Cross-Correlation (NCC), although its computational cost is often prohibitively high for time-sensitive applications. In this work, a previously developed algorithm that uses sum tables to calculate the NCC efficiently for 1-D ultrasound traces is adapted to work for 2-D radar images. The performance of the sum tables algorithm is quantified both theoretically as well as with Synthetic Aperture Radar (SAR) data from the RADARSAT-2 satellite, and is shown to provide time savings of 97% or more compared to the direct method. The algorithm described herein could be used to provide more timely intelligence in situations where it is desirable to detect and estimate the motion of targets using remotesensing.
Crescent sand dunes called barchans are the fastest moving sand dunes in the desert, causing disturbance for infrastructure and threatening human settlements. Their study is of great interest for urban planners and ge...
详细信息
Crescent sand dunes called barchans are the fastest moving sand dunes in the desert, causing disturbance for infrastructure and threatening human settlements. Their study is of great interest for urban planners and geologists interested in desertification (Hugenholtz et al., 2012). In order to study them at a large scale, the use of remotesensing is necessary. Indeed, barchans can be part of barchan fields which can be composed of thousands of dunes (Elbelrhiti et al.2008). Our region of interest is located in the south of Morocco, near the city of Laayoune, where barchans are stretching over a 400 km corridor of sand dunes. We used imageprocessing techniques based on machine learning approaches to detect both the location and the outlines of barchan dunes. The process we developed combined two main parts: The first one consists of the detection of crescent shaped dunes in satellite images using a supervised learning method and the second one is the mapping of barchans contours (windward, brink and leeward) defining their 2D pattern. For the detection, we started by image enhancement techniques using contrast adjustment by histogram equalization along with noise reduction filters. We then used a supervised learning method: We annotated the samples and trained a hierarchical cascade classifier that we tested with both Haar and LBP features (Viola et Jones, 2001;Liao et al., 2007). Then, we merged positive bounding boxes exceeding a defined overlapping ratio. The positive examples were then qualified to the second part of our approach, where the exact contours were mapped using an imageprocessing algorithm: We trained an ASM (Active Shape Model) (Cootes et al., 1995) to recognize the contours of barchans. We started by selecting a sample with 100 barchan dunes with 30 landmarks (10 landmarks for each one of the 3 outlines). We then aligned the shapes using Procrustes analysis, before proceeding to reduce the dimensionality using PCA. Finally, we tested different de
暂无评论