List of Remote Sensing IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Remote Sensing for the year 2015

1. Remote Sensing IEEE 2015 Projects
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List Link : http://kasanpro.com/projects-list/remote-sensing-ieee-2015-projects
Title :Classification of Hyperspectral Image Based on Sparse Representation in Tangent Space
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/hyperspectral-image-classification-based-sparse-representation
Abstract : In many real-world problems, data always lie in a low-dimensional manifold. Exploiting the manifold can
greatly enhance the discrimination between different categories. In this letter, we propose a classification framework
based on sparse representation to directly exploit the underlying manifold. Specifically, using the tangent plane to
approximate the local manifold of each test sample, the proposed method classifies the sample by sparse
representation in tangent space. Unlike several existing sparse-representation-based classification methods, which
sparsely represent the test sample itself, the proposed method sparsely represents the local manifold of the test
sample by tangent plane approximation. Therefore, it goes beyond the sample itself and is more robust to kinds of
variations confronted in hyperspectral image (HSI) such as illustration differences and spectrum mixing. Experimental
results show that the proposed algorithm outperforms several state-of-the-art methods for the classification of HSI
with limited training samples.
Title :Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/hyperspectral-imagery-classification-gabor-feature
Abstract : Sparse-representation-based classification (SRC) assigns a test sample to the class with minimum
representation error via a sparse linear combination of all the training samples, which has successfully been applied
to several pattern recognition problems. According to compressive sensing theory, the l1-norm minimization could
yield the same sparse solution as the l0 norm under certain conditions. However, the computational complexity of the
l1-norm optimization process is often too high for large-scale high-dimensional data, such as hyperspectral imagery
(HSI). To make matter worse, a large number of training data are required to cover the whole sample space, which is
difficult to obtain for hyperspectral data in practice. Recent advances have revealed that it is the collaborative
representation but not the l1-norm sparsity that makes the SRC scheme powerful. Therefore, in this paper, a 3-D
Gabor feature-based collaborative representation (3GCR) approach is proposed for HSI classification. When 3-D
Gabor transformation could significantly increase the discrimination power of material features, a nonparametric and
effective l2-norm collaborative representation method is developed to calculate the coefficients. Due to the simplicity
of the method, the computational cost has been substantially reduced; thus, all the extracted Gabor features can be
directly utilized to code the test sample, which conversely makes the l2-norm collaborative representation robust to
noise and greatly improves the classification accuracy. The extensive experiments on two real hyperspectral data sets
have shown higher performance of the proposed 3GCR over the state-of-the-art methods in the literature, in terms of
both the classifier complexity and generalization ability from very small training sets.
Title :Extracting Man-Made Objects From High Spatial Resolution Remote Sensing Images via Fast Level Set
Evolutions
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/extracting-man-made-objects-from-high-spatial-resolution-remote-sensing-images
Abstract : Object extraction from remote sensing images has long been an intensive research topic in the field of
surveying and mapping. Most past methods are devoted to handling just one type of object, and little attention has
been paid to improving the computational efficiency. In recent years, level set evolution (LSE) has been shown to be
very promising for object extraction in the field of image processing because it can handle topological changes
automatically while achieving high accuracy. However, the application of state-of-the-art LSEs is compromised by
laborious parameter tuning and expensive computation. In this paper, we proposed two fast LSEs for manmade
object extraction from high spatial resolution remote sensing images. We replaced the traditional mean

2. curvature-based regularization term by a Gaussian kernel, and it is mathematically sound to do that. Thus, we can
use a larger time step in the numerical scheme to expedite the proposed LSEs. Compared with existing methods, the
proposed LSEs are significantly faster. Most importantly, they involve much fewer parameters while achieving better
performance. Their advantages over other state-of-the-art approaches have been verified by a range of experiments.
Title :Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral
Image Classification
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/hyperspectral-dimension-reduction-using-spatial-spectral-regularized-local-discriminant
Abstract : Dimension reduction (DR) is a necessary and helpful preprocessing for hyperspectral image (HSI)
classification. In this paper, we propose a spatial and spectral regularized local discriminant embedding (SSRLDE)
method for DR of hyperspectral data. In SSRLDE, hyperspectral pixels are first smoothed by the multiscale spatial
weighted mean filtering. Then, the local similarity information is described by integrating a spectral-domain regularized
local preserving scatter matrix and a spatial-domain local pixel neighborhood preserving scatter matrix. Finally, the
optimal discriminative projection is learned by minimizing a local spatial-spectral scatter and maximizing a modified
total data scatter. Experimental results on benchmark hyperspectral data sets show that the proposed SSRLDE
significantly outperforms the state-of-the-art DR methods for HSI classification.
Title :Aerial Image Registration for Tracking
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/aerial-image-registration-tracking
Abstract : To facilitate the tracking of moving targets in a video, the relation between the camera and the scene is
kept fixed by registering the video frames at the ground level. When the camera capturing the video is fixed with
respect to the scene, detected motion will represent the target motion. However, when a camera in motion is used to
capture the video, image registration at ground level is required to separate camera motion from target motion. An
image registration method is introduced that is capable of registering images from different views of a 3-D scene in
the presence of occlusion. The proposed method is capable of withstanding considerable occlusion and
homogeneous areas in images. The only requirement of the method is for the ground to be locally flat and sufficient
ground cover be visible in the frames being registered. Experimental results of 17 videos fromthe Brown University
data set demonstrate robustness of the method in registering consecutive frames in videos covering various urban
and suburban scenes. Additional experimental results are presented demonstrating the suitability of the method in
registering images captured from different views of hilly and coastal scenes.
Remote Sensing IEEE 2015 Projects
Title :An Adaptive Pixon Extraction Technique for Multispectral/Hyperspectral Image Classification
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/pixon-extraction-technique-multispectral-hyperspectral-image-classification
Abstract : Hyperspectral imaging has gained significant interest in the past few decades, particularly in remote
sensing applications. The considerably high spatial and spectral resolution of modern remotely sensed data often
provides more accurate information about the scene. However, the complexity and dimensionality of such data, as
well as potentially unwanted details embedded in the images, may act as a degrading factor in some applications
such as classification. One solution to this issue is to utilize the spatial-spectral features to extract segments before
the classification step. This preprocessing often leads to better classification results and a considerable decrease in
computational time. In this letter, we propose a Pixon-based image segmentation method, which benefits from a
preprocessing step based on partial differential equation to extractmore homogenous segments.Moreover, a fast
algorithm has been presented to adaptively tune the required parameters used in our Pixon-based schema. The
acquired segments are then fed into the support vector machine classifier, and the final thematic class maps are
produced. Experimental results on multi/hyperspectral data are encouraging to apply the proposed Pixons for
classification.
Title :Saliency-Guided Unsupervised Feature Learning for Scene Classification
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/scene-classification-saliency-guided-unsupervised-feature-learning

3. Abstract : Due to the rapid technological development of various different satellite sensors, a huge volume of
high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from
such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature
learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set
of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an
unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not
need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that
the statistics generated from the learned feature extractors can characterize a complex scene very well and can
produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ
a recently developed regularization method called "dropout," which has proved to be very effective in image
classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the
UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data
set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that
were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the
highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based
scene classification method provides more accurate classification results than the other
latent-Dirichlet-allocation-based methods and the sparse coding method.
Title :A New Framework for SAR Multitemporal Data RGB Representation: Rationale and Products
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/sar-multitemporal-data-rgb-representation
Abstract : This paper presents the multitemporal adaptive processing (MAP3) framework for the treatment of
multitemporal synthetic aperture radar (SAR) images. The framework is organized in three major activities dealing
with calibration, adaptability, and representation. The processing chain has been designed looking at the simplicity,
i.e., the minimization of the operations needed to obtain the products, and at the algorithms' availability in the
literature. Innovation has been provided in the crosscalibration step, which is solved introducing the variable
amplitude levels equalization (VALE) method, through which it is possible to establish a common metrics for the
measurement of the amplitude levels exhibited by the images of the series. Representation issues are discussed with
an application-based approach, supported by examples with regard to semiarid and temperate regions in which
amplitude maps and interferometric coherence are combined in an original way.
http://kasanpro.com/ieee/final-year-project-center-viluppuram-reviews
Title :Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing
Images
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/segmentation-arbitrarily-large-remote-sensing-images-stable-mean-shift-algorithm
Abstract : Segmentation of real-world remote sensing images is challenging because of the large size of those data,
particularly for very high resolution imagery. However, a lot of high-level remote sensing methods rely on
segmentation at some point and are therefore difficult to assess at full image scale, for real remote sensing
applications. In this paper, we define a new property called stability of segmentation algorithms and demonstrate that
pieceor tile-wise computation of a stable segmentation algorithm can be achieved with identical results with respect to
processing the whole image at once. We also derive a technique to empirically estimate the stability of a given
segmentation algorithm and apply it to four different algorithms. Among those algorithms, the mean-shift algorithm is
found to be quite unstable. We propose a modified version of this algorithm enforcing its stability and thus allowing for
tile-wise computation with identical results. Finally, we present results of this method and discuss the various trends
and applications.
Title :Rotation-Invariant Object Detection in Remote Sensing Images Based on Radial-Gradient Angle
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/rotation-invariant-object-detection-remote-sensing-images-based-radial-gradient-angle
Abstract : To improve the detection precision in complicated backgrounds, a novel rotation-invariant object detection
method to detect objects in remote sensing images is proposed in this letter. First, a rotation-invariant feature called
radial-gradient angle (RGA) is defined and used to find potential object pixels from the detected image blocks by

4. combining with radial distance. Then, a principal direction voting process is proposed to gather the evidence of
objects from potential object pixels. Since the RGA combined with the radial distance is discriminative and the voting
process gathers the evidence of objects independently, the interference of the backgrounds is effectively reduced.
Experimental results demonstrate that the proposed method outperforms other existing well-known methods (such as
the shape context-based method and rotation-invariant part-based model) and achieves higher detection precision for
objects with different directions and shapes in complicated background. Moreover, the antinoise performance and
parameter influence are also discussed.
Remote Sensing IEEE 2015 Projects
Title :A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble
Strategy
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/self-training-based-unsupervised-satellite-image-classification-techni
Abstract : This letter addresses the problem of unsupervised land-cover classification of remotely sensed
multispectral satellite images fromthe perspective of cluster ensembles and self-learning. The cluster ensembles
combine multiple data partitions generated by different clustering algorithms into a single robust solution. A
cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative
expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters
considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover
classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is
introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set
obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier,
although trained without any external supervision, reduces the effect of data overlapping from different clusters which
otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a
medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual
clustering of the ensemble.
Title :An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/sift-based-mode-seeking-algorithm-sub-pixel-registration-remotely-sensed-images
Abstract : Several image registration methods, based on the scaled-invariant feature transform (SIFT) technique,
have appeared recently in the remote sensing literature. All of these methods attempt to overcome problems
encountered by SIFT in multimodal remotely sensed imagery, in terms of the quality of its feature correspondences.
The deterministic method presented in this letter exploits the fact that each SIFT feature is associated with a scale,
orientation, and position to perform mode seeking (in transformation space) to eliminate outlying corresponding key
points (i.e., features) and improve the overall match obtained. We also present an exhaustive empirical study on a
variety of test cases, which demonstrates that our method is highly accurate and rather fast. The algorithm is capable
of automatically detecting whether it succeeded or failed.
Title :Remote Sensing Image Segmentation Based on an Improved 2-D Gradient Histogram and MMAD Model
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/remote-sensing-image-segmentation-based-improved-2-d-gradient-histogram-mm
Abstract : A novel remote sensing image segmentation algorithm based on an improved 2-D gradient histogram and
minimum mean absolute deviation (MMAD) model is proposed in this letter. We extract the global features as a 1-D
histogram from an improved 2-D gradient histogram by diagonal projection and subsequently use the MMAD model
on the 1-D histogram to implement the optimal threshold. Experiments on remote sensing images indicate that the
new algorithm provides accurate segmentation results, particularly for images characterized by Laplace distribution
histograms. Furthermore, the new algorithm has low time consumption.