Krishna Kant Singh

Shadow Detection and Removal from Remote Sensing Images using NDI and Morphological Operators

Shadows appear in remote sensing images due to elevated objects. Shadows cause hindrance to correct feature extraction of image features like buildings ,towers etc. in urban areas it may also cause false color tone and shape distortion of objects, which degrades the quality of images. Hence, it is important to segment shadow regions and restore their information for image interpretation. This paper presents an efficient and simple approach for shadow detection and removal based on HSV color model in complex urban color remote sensing images for solving problems caused by shadows. In the proposed method shadows are detected using normalized difference index and subsequent thresholding based on Otsu’s method. Once the shadows are detected they are classified and a non shadow area around each shadow termed as buffer area is estimated using morphological operators. The mean and variance of these buffer areas are used to compensate the shadow regions.

A Fuzzy Kohonen Local Information C-Means Clustering for Remote Sensing Imagery

ABSTRACT This paper presents a neuro fuzzy clustering algorithm, Fuzzy Kohonen Local Information C-Means (FKLICM), for classification of remote sensing images. The proposed algorithm is a hybridization of the conventional Kohonen clustering network and Fuzzy Local Information C-Means (FLICM) to produce a much more efficient and accurate clustering algorithm. The proposed algorithm first forms a fused image with three Multispectral bands and pan band of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using the Brovey transform. The fused image is a three band image with higher resolution and better visual perception. The fused image is reduced to a one-dimensional image using principal component analysis (PCA). The FKLICM algorithm is applied on the PC-1 image to classify the remote sensing image into different land cover types. Integrating the neural network with a fuzzy system combines the advantages and overcomes the limitations of both technologies. The experimental results of the proposed algorithm are compared with two other algorithms, FCM and GIFP-FCM. The classification results and accuracy assessment show that FKLICM yields better results than the other methods.

A novel edge preserving filter for impulse noise removal

This paper proposes an edge preserving filter for removal of impulse noise. Digital images received from various sources are often degraded due to impulse noise and thus become unsuitable for further processing. To overcome this degradation removal of impulse noise is very important. In this paper an effective and efficient method of impulse noise removal is proposed which not only removes noise but also preserves edges. The algorithm first finds noisy, noise free and edge pixels. Then it replaces the noisy pixel with a pixel from its neighbourhood which is nearest to the adaptive median of the noisy pixel, this removes the noise as well as preserves edges and fine image details.

A Novel Algorithm for Impulse Noise Removal and Edge Detection

Edge detection of images is an important task in computer vision and image processing. Edge detection of noise free images is relatively simpler, but in most practical cases the images are degraded by noise. To find the edges from noisy images is a challenging task. This paper proposes a novel edge detection algorithm for images corrupted with noise. The algorithm finds the edges by eliminating the noise from the image so that the correct edges are determined. For making the image noise free the algorithm calculates closeness parameters, based on this parameter the noisy pixel is replaced by the most appropriate value. The edges of the noise free image are determined using morphological operators erosion and dilation. The proposed algorithm uses a combination of these operators to find the edges. This algorithm uses two different types of structuring elements so that all the edges of the image are determined efficiently.

Satellite image classification using Genetic Algorithm trained radial basis function neural network, application to the detection of flooded areas

Development of an image classification method for satellite image classification.Use of spectral indices for feature extraction.Development of GA trained RBFNN for better results.Classification of latest Landsat 8 OLI images.Application of the proposed method for identification of flooded areas. In this paper, a semi supervised method for classification of satellite images based on Genetic Algorithm (GA) and Radial Basis Function Neural Network (RBFNN) is proposed. Satellite image classification problem has two major concerns to be addressed. The first issue is mixed pixel problem and the second issue is handling large amount of data present in these images. RBFNN function is an efficient network with a large set of tunable parameters. This network is able to generalize the results and is immune to noise. A RBFNN has learning ability and can appropriately react to unseen data. This makes the network a good choice for satellite images. The efficiency of RBFNN is greatly influenced by the learning algorithm and seed point selection. Therefore, in this paper spectral indices are used for seed selection and GA is used to train the network. The proposed method is used to classify the Landsat 8 OLI images of Dongting Lake in South China. The application of this method is shown for detection of flooded area over this region. The performance of the proposed method was analyzed and compared with three existing methods and the error matrix was computed to test the performance of the method. The method yields high producers accuracy, consumers accuracy and kappa coefficient value which indicated that the proposed classifier is highly effective and efficient.

Unsupervised change detection from remote sensing images using hybrid genetic FCM

This paper presents a new technique for unsupervised change detection in bitemporal remote sensing images using spectral change difference images and hybrid genetic FCM. The proposed method works in three steps. In the first step, three spectral change difference images:absolute value difference image, ratio image and log ratio image are computed. In the next step, a feature vector space is created using PCA. Finally, the change detection is obtained by dividing the feature vector space into two clusters using genetic FCM. The validity of the clusters is measured by DB index. The parts of image of Reno-Lake Tahoe area was used as data set for the performance evaluation of proposed algorithm. The results obtained were compared with EM based, MRF based and NSCT methods. The results verify that the proposed algorithm provides superior results than the other existing methods.

A N 8 (P) detail preserving adaptive filter for impulse noise removal

This paper proposes a N8(p) detail preserving adaptive filter for impulse noise removal. Impulse noise degrade the digital images due to which these images cannot be used for high level processing. Thus, image restoration becomes important. In this paper an effective and efficient method of impulse noise removal is proposed which not only removes noise but also preserves image details. The algorithm first classifies all the pixels as noise and noise free based on its N8(p) neighbours using averaging parameters introduced here and then replaces the noise pixels by the adaptive median of the pixel. The algorithm uses adaptive median as it provides better denoising and since the proposed algorithm performs prior classification of pixels as noise and noise free this preserves image details.

Detection of tsunami-induced changes using generalized improved fuzzy radial basis function neural network

Abstract The coastal areas of Japan were hard hit by a magnitude 9.0 earthquake on 11 March 2011. The earthquake triggered a disastrous tsunami over the area which led to massive destruction. In this paper, tsunami-induced changes in Soma, Watari, Natori and Iwanuma areas using Landsat 7 ETM+ and EO-1 ALI images are identified. The proposed method is based on image classification using radial basis function neural network and generalized improved fuzzy partition FCM algorithm. The pre- and post-tsunami images of the area are first classified using a radial basis function neural network. The pre- and post-tsunami images are classified into three classes including water, vegetation and urban and bare land class. The classified images are compared with other to obtain a set of four change classes. These change classes are labelled to obtain a classified change map. The change map reveals that large areas of vegetations and urban land are washed away by the tsunami in all the four cities, Soma, Watari, Natori and Iwanuma. The accuracy assessment of the method shows that the results obtained are quite satisfactory. The method has high overall accuracy and kappa coefficient value.

Detection of 2011 Tohoku Tsunami Inundated Areas in Ishinomaki City Using Generalized Improved Fuzzy Kohonen Clustering Network

Abstract In this paper, a framework for identifying tsunami inundated areas using an innovative Generalized Improved Fuzzy Kohonen Clustering Network (GIFKCN) is proposed. GIFKCN hybridizes the Kohonen clustering network with Generalized Improved Fuzzy Partitions FCM (GIFP-FCM) algorithm to build a more efficient and effective neuro fuzzy classifier. GIFKCN classifier combines the advantages of both a neural network and fuzzy systems. A number of spectral indices are computed and the mean values of these indices are used to train the GIFKCN classifier. The novel classifier was applied to identify March 2011 Tohoku tsunami inundated areas in Ishinomaki city. The performance of the classifier is satisfactory with high overall accuracy and Kappa coefficient.

Classification of bamboo plant based on digital image processing by Central moment

There is advancement in every day for image classification starting from object classification to remote sensing image. Plant classification from their part is one of the most current research works going in the area of image processing. The proposed work is a new approach for bamboo species classification from their Culm sheath by using Central moment. Automated recognition of bamboo has not yet been well established mainly due to lack of research in this area, non-availability and difficulty in obtaining the database. Therefore need of recognition of bamboo species is required by the user. The proposed work is an automated classification of bamboo species system based on shape features of bamboo Culm sheath by using the central moment classifier. Four different bamboo species are taking for experiment in the proposed work. The results obtained shows considerable recognition accuracy proving that the techniques used is suitable to be implemented for commercial purposes.