中文摘要：

随着遥感技术的突飞猛进,遥感图像目标识别在军事方面以及民用方面都有重要的应用.但是在对遥感图像目标识别的过程中,由于遥感图像的高分辨率等客观条件限制,无法实现对目标实时和精确的识别.极速学习机具有很快的学习速度并且是一次完成的,在小样本学习的问题中得到了广泛的应用.可以先对遥感图像进行特征提取,然后用极速学习机的神经网络方法对遥感图像目标进行识别,这是解决问题的一种有效方法.本文首先在极速学习机的基础上针对极速学习机隐层神经元过多的问题进一步提出隐层神经元数目自动确定的自适应极速学习机的算法,然后介绍了遥感图像特征提取的方法,最后通过实验仿真验证自适应极速学习机算法在遥感图像目标识别上的准确性和实用性.

英文摘要：

Now,with the rapid progress of remote sensing technology,remote sensing target recognition has a very important practical significance and wide range of applications whether in civilian or military fields.However,due to high-resolution remote sensing images and other objective conditions,we can＇t achieve real-time and accurate target recognition in the process of remote sensing image target recognition.Extreme learning machine（ELM）is a singlehidden layer feedforward neural network（SLFN）with at most N hidden nodes and with almost any nonlinear activation function can exactly learn N distinct observations.It should be noted that the input weights（linking the input layer to the first hidden layer）and hidden layer biases need to be adjusted in all these previous theoretical research works as well as in almost all practical learning algorithms of feedforward neural networks.ELM has the advantage of fast learning speed and is a one-time learning method,which has been widely used in small-sample learning prob-lems.Aircraft and ships as typical artificial objects,having regular shape features,can be regarded as ideal geometric primitives,so aircraft and ship target can be seen as different category shapes.Firstly,to extract remote sensing images feature,and then ELM is applied to remote sensing target recognition,which is an effective way to solve the problem.However,ELM has the disadvantage of profuse hidden nodes（usually the same with number of learning samples）and huge model structure.Profuse hidden nodes inevitably lead to decrease the computation speed of the model.Therefore,the paper firstly puts forward a learning method based on ELM in which the number of hidden layer neurons is determined automatically— adaptive extreme learning machine（adaptive-ELM）which overcomes the disadvantage of profuse hidden nodes in ELM network,and then introduces remote sensing image feature extraction methods.The adaptive-ELM method firstly adaptively determines the number of hidden layer neurons through affini