Remote sensing image classification is a prerequisite for remote sensing applications, such as thematic mapping, urban
planning, forest management, environment monitoring, disaster warning and assessment, military target recognition.
Over the last decade there has been noticeable shift in remote sensing image classification with the extension of remote
sensing imagery sources as well as the development of pattern recognition methods. This paper discusses the changes in
remote sensing classification from two aspects: basic thought and new classification algorithms. The basic thought of
remote sensing classification has changed from per-pixel multispectral-based approaches to multiscale object-based
approaches. New classification algorithms include support vector machine, evolutionary algorithm, fuzzy clustering
algorithm, as well as Artificial Neural Networks. At last this paper highlights the future research and application
directions of remote sensing image classification.
Up to now, TERCOM hasn't been used in the gravity matching aided navigation, because of its disability in precise
positioning. So does ICCP, which needs precise initial values, whereas the initial values provided by INS are not
accurate enough. This paper starts from the introduction of the basic principles of TERCOM and ICCP, and compares
their advantages and disadvantages, then incorporates these two methods into a new algorithm in which TERCOM
provides the initial position and ICCP does the precise positioning. At last simulation data is used in the experiments,
with the purpose to shed light on the effect of this new algorithm in gravity matching aided navigation. It is proved that
the new method is precise and needs no initial values. It is very fit for gravity matching aided navigation.
Image matching algorithm is the core of image matching aided navigation system. This paper has given a quick and
effective image matching algorithm based on Fourier-Mellin transform. It solves relative translation of two images by
calculating their peak value's position coordinates through inverse Fourier transform of power spectrum. A great deal of
experiments indicates that this matching algorithm has high precision and strong robustness. At last, a proposal was
advanced to image matching aided navigation system.
In order to find the precise position of the underwater vehicles, suitable matching areas are needed to be decided in the
Gravity-matching Aided Navigation System. This paper presents the segmentation suitable-matching areas method of
navigation reference map. Firstly, the paper put forward the stability of matching index based on every gridding points in
the reference map. The discussion indexes are roughness, main peak curvature of correlation function, trackability, and
characteristic density. According to these measures, the suitable matching area was divided by curve evolution using
level set method. Several simulations and experiments were performed in this paper.
Extremely precise texture modeling of complex 3D objects is a significant problem worth of much research. Comparing the laser scanning technology and the digital photogrammetry with their combination results, the optimal solution is the fusion of the data. Surveying points are used to model the main shapes while laser scanning captured the fine details. Photogrammetry is used to register the texture images with the geometry and produce ortho-texture. This paper explains a reliable method based on Direct Linear Transformation (DLT) and ortho-rectification method to model 3D objects combined with 3D laser scanning data and high-resolution image data. This novel technology can produce precise texture and improves the efficiency and quality of highly detailed texture modeling.
Three-line scanner CCD stereo surveying camera is now considered to be used not only in space borne vehicle but also airborne one. It can capture three images of a stereo pair on the same orbital pass. The imagery is characterized by invalidation of conventional collinear equation geometric model and impropriety of satellite borne sensor model. This paper addresses the method of simulating the change of exterior orientation parameters and making analog airborne three-line scanner (TLS) CCD image. Besides, we will present the accuracy test results processed with three kinds of camera geometric models. Results show that the precision of collinear-equation-based polynomial Model with root mean square error (RMSE) less than one pixel is better than other.
This paper discusses the existing three optimal band combination rules of hyperspectral remote sensing images. They are joint entropy, optimal index factor and Sheffield index respectively. Three bands of MODIS images data are combined arbitrarily according to the three rules, so the best three bands combination images of the three rules are acquired. On the basis of this, the three images are all classified in term of maximum likelihood classifier. Also, the influence of each band combination to the classification performance is discussed. The experiment result proves that the best classification performance of the MODIS images based on the three bands combination is the combination image based on optimal index factor.