In this topic you have explored concepts, examined algorithms and developed skills of processing EO data. Your studies have focused on information contained in the spatial domain. The spatial domain is very important in Earth observation by remote sensing. The most significant element is that of spatial scale or resolution as this effectively controls the information content present within a remotely sensed image.

Remote Sensing and Spatial Resolution
The spatial resolution of remotely sensed imagery is a strong controlling factor in determining which objects can be distinguished within a scene. Spatial resolution can pose problems in operational remote sensing associated with the ‘mixed pixel’ problem, where pixels are characterised by heterogeneity rather than purity. Image variance is directly related to the size of target objects present within the image scene and the spatial resolution of the imaging sensor so the choice of imagery will be application dependent. As a budding image analyst you should be able to select the most appropriate sensor type and spatial resolution for a particular remote sensing application.

Image Enhancement
The spatial structures present within an image scene may be preferentially enhanced by a range of algorithms. The most widely used technique is the spatial frequency filter which allows the analyst to emphasise or suppress different spatial frequency components including large homogenous area, edges and directional features. An alternative technique, the Fourier Transform allows image analysts to process images using an alternative co-ordinate space known as the power spectrum, which highlights spatial frequencies and orientations present within the original image scene. This technique can be used to remove spatial noise present within an image scene or alternatively as an improved (though computationally demanding) filtering method. The variogram is a measure of spatial dependence variance within a remotely sensed image scene. It can be used in image interpretation and allows the image analyst to determine the presence, and distribution, of any spatial structures or patterns within the scene.

Spatial Models
The analysis of the spatial components of remotely sensed imagery can be performed by visual interpretation or by automated techniques using texture and contextual information. Spatial properties such as texture, scale, pattern and context may be employed in the analysis and interpretation of remotely sensed imagery. The use of regions or ‘objects’ within a scene allows the image analyst to combine spatial and spectral information in the interpretation and classification of images.

Multisource Remote Sensing
The processes (and the scales of these processes) operative within the natural and human landscape are many and varied. Often these processes cannot be identified or analysed using single source imagery. Multisource remote sensing, often known as fusion techniques, allow the analyst to combine the spatial and spectral properties of different images and allow the discrimination of finer target objects from the image scene.