A new watershed segmentation (NWS) and particle swarm optimization (PSO-SVM) techniques in remote sensing image retrieval

In this paper, the latest watershed segmentation process is actually found in the object feature extraction process. In the proposed method, initially the actual visual features are usually taken from the images using the spatial spectral heterogeneity method. Afterwards, the object features are usually taken from the new watershed segmentation method in which segmented objects are usually grouped with the PSO-SVM method. With PSO-SVM, the actual SVM parameters are usually optimized to achieve higher classification accuracy. Then similar scene images from the data base are usually taken from the SS modelling. A further variety of remote sensing images are utilized in the overall performance analysis process. The particular implementation benefits show the effectiveness of proposed new watershed segmentation method in RSIR and the reached advancement in sensitivity and also recall measures. Moreover, the actual overall performance of the proposed technique is actually considered by comparing with all the existing RSIR and the typical SBRSIR methods.