For implementing change detection approaches in image processing domain, spectral limitations in remotely sensed images are remaining as an unresolved challenge. Recently, many algorithms have been developed to detect spectral, spatial, and temporal constraints to detect digital change from the synthetic aperture radar (SAR) images. The unsupervised method is used to detect the appropriate changes in the digital images, which are taken between two different consecutive periods at the same scene. Many of the algorithms are identifying the changes in the image by utilizing a similarity index-based approach. Therefore, it fails to detect the original changes in the images due to the recurring spectral effects. This necessitated the need to initiate more research for suppressing the spectral effects in the SAR images. This research article strongly believes that the unsupervised learning approach can solve the spectral issues to correct in the appropriate scene. The convolutional neural network has been implemented here to extract the image features and classification, which will be done through a SVM classifier to detect the changes in the remote sensing images. This fusion type algorithm provides better accuracy to detect the relevant changes between different temporal images. In the feature extraction, the semantic segmentation procedure will be performed to extract the flattened image features. Due to this procedure, the spectral problem in the image will be subsided successfully. The CNN is generating feature map information and trained by various spectral images in the dataset. The proposed hybrid technique has developed an unsupervised method to segment, train, and classify the given input images by using a pre-trained semantic segmentation approach. It demonstrates a high level of accuracy in identifying the changes in images.

Machine learning Spectral constraints SAR images