![]() ![]() To reduce the increasing demand of storage space and transmission time compression techniques are the need of the day. Image compression is an important tool to reduce the bandwidth and storage requirements of practical image systems. In addition to the classification method, image segmentation also plays an important role in the accuracy of the results. The support vector machine is recommended for further research. The best result of the k-nearest neighbour method was obtained with the highest compression ratio (100:1). Classification accuracy of the support vector machine method indicates that compression ratios of up to 30:1 can be used without any loss of classification accuracy. moreover, in some cases classification of compressed images yields better results than classification of the original image. The study proves that in general lossy compression does not adversely affect the classification of images. The results explore the impact of compression on the images, segmentation and resulting classification. This paper presents the effects of JPEG2000 lossy compression on the classification of very high-resolution WorldView-2 imagery For the first dine, the k-nearest neighbour and support vector machine methods of the object based classification were used. Lossy compression is becoming increasingly used in remote sensing, although its effect on the processing results has yet not been fully investigated. In addition to the classification method, image segmentation, a basic step of object classification, plays an important role in the accuracy of the results. In the study we found that the support vector machine method gives better classification results than the k-nearest neighbor and is also recommended for further research. The best result of the k-nearest neighbor method was obtained with the highest compression ratio (100:1), but the outcome cannot be trusted without reserve. Classification accuracy of support vector machines method indicates that compression ratios of up to 30:1 can be used without any loss of accuracy. The study proves that in general lossy compression does not adversely affect the classification of images what is more, in some cases classification of compressed images gives better results than classification of the original image. The k-nearest neighbor and support vector machine methods of the object based classification were used and compared. This paper presents the effects of JPEG 2000 lossy compression on the classification of very high-resolution WorlView-2 imagery. ![]() ![]() Lossy compression is becoming increasingly used in remote sensing although its effect on the processing results has yet not been fully investigated. Finally, the confusion matrices of the classified images were generated and evaluated to determine the effect of compression and fusion techniques on the accuracy of the classification process. Then, the compressed fused images were classified using Maximum Likelihood Classification and Artificial Neural Network Classification techniques. The fused image with the superior accuracy was then compressed with various compression ratios ranging from 1:10 to 1:100. In this study, two pan and mul Geo-eye images covering an area of Cape Town, South Africa were registered and fused using different fusion techniques. The purpose of this paper is to study the effect of image compression and fusion techniques on the classification accuracy. In addition, image fusion which is the merging of panchromatic and multispectral images to generate a single image with high spatial and spectral resolutions is required to increase the information in the resulted image. Nowadays, the remotely sensed images are of huge sizes that require the implementation of compression technique to be easily stored on the internet. ![]()
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