B. Mamatha
Ocean surveillance is becoming significant since the military coastal activities are becoming more and more frequent, the ship types are increasing fast and the unconventional activities happen often. The traditional sea surveillance methods of monitoring the sea targets only by the metric information such as range, aspect, speed and direction of the target is do not giving proper results for identification of a ship. So, there is a requirement to develop automatic ship recognition systems. A few such recognition systems with ship detection software already exist. Now the challenge is to quantify detection capability and to design new software systems to better detect ship signatures to classify the ship targets of interest. In this work an Feature selection and extraction of Inverse Synthetic Aperture Radar (ISAR) image for ship classification is taken as a research problem. Ship ISAR image is a high resolution radar image. Radar is a popular sensor for it can be used to acquire target signatures in all conditions and also from hundreds of kilometers of distance. The primary objective is to acquire target characteristics or features from the target signatures acquired through radar. Then these features are used for recognition and classification of targets. ISAR images are radar images acquired through high resolution radar which are widely being used for recognition of sea, air, land and even human targets since they contain useful information of target geometry.
In this work a set of ship ISAR images measured with high resolution radar are taken as a data set. The sea clutter and noise from nearby terrain causes hindrance to obtain clear ship ISAR images. Hence preprocessing of ISAR images is the first step and extraction of ship features having highly discriminative nature and low dimensionality from noise free ISAR images is the next step in ship classification. The problem of classification can be solved by using neural networks. Different feature extraction techniques are applied to the preprocessed ISAR images from the data sets. The features computed from training data set are used to train the classifier and the features obtained from test set are utilized to quantify the classification accuracy of the feature vector. In this work probabilistic neural network is used as a classifier. In this work study is carried out to identify feature vectors that classify the unique set of ship ISAR images considered in this work. Various statistical techniques and mathematical transforms are used to derive features from ship ISAR images. The statistical descriptors and Zernike moments are found to give good classification accuracy.
ISAR images are wave decomposed up to five levels with the application of multi resolution wavelet transform. For each wave, decomposed level Image, all approximation and detailed wave coefficients are computed and taken as feature vectors to identify the best suitable wave coefficients for the unique set of ship ISAR images considered in this work. Segmentation is used to find regions of interest in case of medical images, border or shape information of ISAR images or to identify linked homogeneous regions in high resolution remote sensing image analysis applications. Hence keeping in view of the significant role of segmentation in image analysis, above mentioned wave decomposed image features are also studied to find the classification accuracy of the ISAR images with segmentation.
Ship ISAR images are color images. The moments computed from individual color component images R,G,B and combined color component images RG,GB,RB of the ISAR images taken together are considered as feature vector. Using the multi resolution wavelet transform, the ISAR images are decomposed up to five levels. The average wave energy values are computed for each of the wave decomposed images. Obtained wave energy values are found to form a good feature vector for the ship ISAR image classification.
In classification problems, the feature vector plays a significant role since the computational complexity and accuracy of classification depend on the features that constitute a feature vector and also the number of features in the feature vector. All the features in a feature vector under consideration may not equally contribute to the classification of the images. Some of them may be redundant. Since a feature vector of smaller size with high discriminating nature is always preferred. Hence there is always a need to select an optimum feature set to solve the classification problem at hand. Several available optimization techniques can be explored to select optimum feature set. In this research work, ship ISAR image classification problem is studied as a single objective optimization problem and the different optimum feature combinations that give the satisfactory classification for the considered data set are identified. The optimization techniques like genetic algorithm and particle swarm optimization techniques are employed to identify the optimum feature sets in case of color moments and wave energy levels.