## 4 Discussion  
Remote sensing data comprises of spatial, spectral and temporal resolution. Spectral statistics is widely made use in remote sensing image classification. The main aspect in accuracy of ground object is spatial resolution. Temporal resolution helps to generate land cover maps for environmental planning, land use change detection, urban planning and so forth.  
Image enhancement improves the image quality and information content before further processing is carried out. Some commonly used techniques include contrast enhancement, spatial filtering and so forth. The linear contrast enhancement is best applied to remote sensing images with Gaussian or near-Gaussian histograms where all the brightness values generally fall within a single narrow range of the histogram. But there occurs a situation mainly for scenes with large land and water bodies. Non-linear contrast enhancements can be applied on low-contrast imagery of which histogram equalization is a prominent technique. Non fusion based enhancement gives low spatial information but with high computational complexity. To overcome the limitation of high complexity, fusion based enhancement is used.  
Pixel-based feature extraction methods are used to extract low-level features, which do not take into account the information about spatial relationships. Low-level features are extracted directly from the raw, noisy pixels with edge detection being the most commonly used algorithm. Object-based approaches are used to get high-level features, which represent shapes in images that are detected irrespective of illumination, translation, orientation and scale. High-level features are extracted depending on the information from low-level features. High-level feature extraction is mainly used for automated object detection and extraction. Depending on the spatial resolution of the source image, different segmentation algorithms are used. For low to medium resolution imagery, clustering algorithms are better choice, however for high resolution imagery, multiresolution segmentation offers better results. On a high spatial resolution image, detailed geometric features can easily be identified, while the multispectral images contain richer spectral information. The capabilities of the images can be enhanced if the advantages of both high spatial and spectral resolution can be integrated into one single image. The detailed features of such an integrated image thus can be easily recognized and will benefit many applications, such as urban and environmental studies.  
In change detection, it is difficult to find the method which is suitable for finding the changes which have occurred. Selection of suitable technique for change detection is affected by various factors like variation of physical characteristics of features with time, improper registration of the images, effect of cloud/haze and so forth and it is quite a difficult task to find a single method since the nature of problem determines which method is best suited. Techniques like image differencing and image ratioing can be used only when the change and no-change information is necessary. If a detailed matrix is required, post classification change detection is a good choice. Image-classification techniques, either pixel-based or object-based, are used for converting an image into a thematic map. The creation of digital surface and terrain models (DSMs/DTMs) from satellite imagery has become a common method to analyze the structure and development of vegetation and geomorphological landforms.  
Recently, image processing through quantum computation also called quantum image processing (QIP) and application of machine learning through quantum computation or quantum learning (QL) have provided a new perspective to large-scale image processing. QIP uses quantum properties to encode images for improving storage and time efficiency of certain operations like image rotation. Thus quantum algorithms are being widely extended to improving digital or conventional image-processing applications and missions.