Akansha Singh

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.

Improving Acquisition of Phosphorus and Other Nutrient Elements by Baby Corn (Zea mays L.) through the Combine Use of Biochar, Phosphorus and Arbuscular Mycorrhiza

For achieving high yields farmers are compelled to adopt different types of management practices like intensive cultivation and enhanced use of agrochemicals mainly fertilizers and pesticides that cause remarkable change in the environment and under many condition leading to deterioration of soil quality and decline in soil organic carbon at different places of India. Therefore, the task of attaining higher productivity of crops and maintaining soil quality at the same time could be achieved by integrating new approaches that involve use of low cost organic soil amendments, conditioners. One such sustainable technology is application of biochar. Biochar can enhance growth of plants and improve physical, chemical and biological properties of soil.

Unsupervised Change Detection in Remote Sensing Images Using CDI and GIFP-FCM Clustering

In this paper, an unsupervised change detection method for remote sensing image is proposed. The method takes as input two bi temporal images and outputs a change detection map highlighting changed and unchanged areas. Initially, the method creates a feature vector space by performing the principal component analysis (PCA) of both the images. The first component of PCA of both the images is used to compute the combined difference image. A change map is then created from combined difference image by clustering it into two clusters changed and unchanged using GIFP-FCM clustering technique. The method is applied on Landsat 5 TM images and the results obtained are compared with other existing state of the arts method.