The field of digital image processing has experienced continuous and significant expansion in recent years. The usefulness of this technology is apparent in many different disciplines covering medicine through remote sensing. The advances and wide availability of image processing hardware has further enhanced the usefulness of image processing.

近年来，数字图像处理领域不断扩大，从医学领域到遥感技术，数字图像处理技术在很多学科都得到了显著的应用。图像处理硬件的发展和广泛的适应性也进一步促进了图像处理的应用。
Remote sensing is the process of collecting data about objects or landscape features without coming into direct physical contact with them.
遥感技术是远距离地采集对象或地形特征的数据，而不需要物质的直接接触。
Digital Image Processing is not only a step in the remote sensing process, but is itself a process that consists of several steps. It is important to remember that the ultimate goal of this process is to extract information from an image that is not readily apparent or is not available in its original form. The step taken in processing an image will vary from image to image for multiple reasons, including the format and initial condition of the image, the information of interest (i.e., geologic formations vs. land cover), the composition of scene elements. There are three general steps in processing a digital image; preprocessing, display and enhancement, and information extraction.
数字图像处理不仅是遥感处理过程中的一个环节，它本身也包括几个步骤。重要的是要记住这个过程的最终目的是从原始形式不明显或不可用的图像中提取信息。处理图像所采取的步骤根据不同的图像会有所不同。原因如下：图像的格式和初始条件不同、对信息的兴趣不同（例如地质层对上地覆盖）以及环境成分不同。处理数字图像一般有三个步骤：预处理、显示和增强、信息提取。
Preprocessing—Before digital images can be analyzed, they usually require some degree of preprocessing. This may involve radiometric corrections, which attempt to remove the effects of sensor errors and/or environmental factors. A common method of determining what errors have been introduced into an image is by modeling the scene at the time of data acquisition using ancillary data collected.
预处理——在分析数字图像之前，经常需要对图像进行某种程度的预处理。包括辐射校正，这种方法的目的是去除传感器错误和环境因素的影响。断定什么错误已经传入图像的常用方法，是通过利用收集到的辅助数据对获得数据时的景象进行建模。
Geometric corrections are also very common prior to any image analysis. If any types of area, direction or distance measurements are to be made using an image, it must be rectified if they are to be accurate. Geometric rectification is a process by which points in an image are registered to corresponding points on a map or another image that has already been rectified. The goal of geometric rectification is to put image elements in their proper planimetric (x and y) positions.
几何校正也是常用的优于其他图像分析的方法。如果需要利用图像区域、方向或距离，需要对它们精确调整。几何校正是图像中的点与地图或另一个已校准的图像中相应的点配准的过程。几何校正的目的是将图像元素放在适当的平面位置。
Information Enhancement—There are numerous procedures that can be performed to enhance an image. However, they can be classified into two major categories: point operations and local operations. Point operations change the value of each individual pixel independent of all other pixel, while local operations change the value of individual pixels in the context of the values of neighboring pixels. Common enhancements include image reduction, image magnification, transect extraction, contrast adjustments (linear and non-linear), band ratioing, spatial filtering, fourier transformations, principle components analysis, and texture transformations.
信息增强——有几种方法可以增强图像。这些方法可以分成两大类：点运算和局部运算。点运算独立地改变每一个像素的值，而局部运算根据相邻像素的值改变每一个像素值。常用的增强方法包括图像缩小、图像放大、横断而提取、对比度调节（线形或非线形）、带比调节、空间滤波器、立里叶变换、主成分分析和纹理转换。
Information Extraction—Unlike analog image processing, digital image processing presently relies almost wholly on the primary elements of tone and color of image pixels.
信息提取——与模拟图像处理不同，当前的数字图像处理几乎仅依赖于像素的色调和颜色这些基本要素。
There has been some success with expert systems and neural networks which attempt to enable the computer to mimic the ways in which humans interpret images. Expert systems accomplish this through the compilation of a large database of human knowledge gained from analog image interpretation which the computer draws upon in its interpretations. Neural networks attempt to "teach" the computer what decisions to make based upon a training data set. Once it has "learned" how to classify the training data successfully, it is used to interpret and classify new data sets.
专家系统和神经网络试图让计算机模拟人类理解图像的方式，已经获得了一些成功。专家系统通过编辑模拟图像解译得到的大型人类知识库，由计算机利用图像解译实现模拟。神经网络试图“教”给计算机基于一组训练数据作出什么判决。当它成功地学会了怎样分类训练数据，就被用来理解和分类新的数据组。
Once the remotely sensed data has been processed, it must be placed into a format that can effectively transmit the information it was intended to. This can be done in a variety of ways including a printout of the enhanced image itself, and image map, a thematic map, a spatial database, summary statistics and/or graphs. Because there are a variety of ways in which the output can be displayed, a knowledge not only of remote sensing, but of such fields GIS, cartography, and spatial statistics are a necessity. With an understanding of these areas and how they interact one with another, it is possible to produce output that give the user the information needed without confusion. However, without such knowledge it is more probable that output will be poor and difficult to use properly, thus wasting the time and effort expended in processing the remotely sensed data.
当遥感数据被处理后，它需要设置成能有效传输信息的格式。这可以通过几种方法实现，包括打卬出增强图像本身、图像映射图、专题地图、空间数据图、摘要统计和图表。因为有多种方式可以显示输出，不仅遥感知识，地理信息系统的领域、绘图法和空间统计也是必需的.理解了这些领域和它们的交叉，就可以清晰地输出用户需要的信息。无论如何，如果没有这些知识，输出会很差并且很难得到适当的应用，这样就会浪费花费在遥感数据处理上的时间和努力。