Auto-identify Classification Technology For LUCC Information Based On Remote Sensing Data

【Objective】Extracting accurate land use/land cover change informantion based on TMimages acquired in Augest2,1986and August17,2003, chosing Hengshan County located inthe farming-pastoral ecotone of Northern Shaanxi as study area.【Method】Firstly, the remotesensing images were pretreated by field survey and thematic maps. After that, methods ofvisual interpretation for each LUCC type were established by multi-feature knowledge.Secondly, different feature transformation and image enhancement methods were investigated,including principal component analysis, tasseled cap transformtion, LBV transformtion,wavelet filtering etc., then comparative analysis of the results were carried out. Thridly,spectral indices of waters, sandy land and residential area were established and described.Validation of object-oriented image segmentation for each LUCC type was carried out. Theclassification accuracy of4methods were compared, including minimum distance, maximumlikelihood, BP artificial neural net and support vector machine. On this basis, the thematicclassification flows for each LUCC type were designed by combining the hierarchical theoryand object-oriented image segmentation. Subsequently, results were visually inspected andquantitatively evaluated by test samples. Evaluation indices included map accurary, user'saccuracy, Kappa coefficient and overall accuracy rate. Finialy, On the basis of comparison ofclassification results, analysis of LUCC changes in Hengshan County from1986to2003wasrealized from the aspects of quantity change, speed change and land type change【.Result】Thegeneral information of residential area, waters, sandy land and waste grassland extracted fromTM image is more accurate by using threshold segmentation of CWI, CRI and CSI. Exceptfor sandy land and waste grass land, the accuracy of object-oriented image classification ismuch better than that of pixel-based classification, as well as salt and pepper effect hasdecreased substantially. Using method of combining object-oriented image segmentation andhierarchical theory,the LUCC thematic information extracted from TM images is moreaccurate and reliable than that of direct supervised classification schema.【Conclusion】Hengshan County is located in the transition zone of the southeast of the Mu Us Desert andloess hilly and gully region, having complex terrain and topography. As a result, fragmentizedpatches, mixed pixel, shadow, metameric substance of same spectrum, metameric spectrum of same substance are ubiquity in TM images. These factors bring too many difficulties inextracting thematic information using direct supervised classification schema. Theseclassification results can't be in the production of thematic maps and dynamic monitoring ofLUCC because of obvoius salt and pepper effect and low accuracy. Focused on theseproblems, this paper presents a thematic information extraction method of successiveapproximation. Based on this method the optimum sequence arrangement of hierarchicalextraction schema has been realized. And validation by visual inspection and quantitativeevaluation show that hierarchical extraction procedures is beneficial to decreasingmisclassification error the rate of wrong classification and rate of miss classification, theaccuracy and efficiency of LUCC thematic information extracion is improved substantially.Specific research content and its related innovation are carried out as following:(1) Wavelet soft-thresholding filtering to TM image data using Coiflet (order=3) asmother wavelet can give remarkably good results in removing isolated fine patches and pixelsinset bigger blocks and enhancing homogeneity of patches, while maintains image definitionwell. This set the stage for ratio-difference indices as NDVI, MNDWI, NDBI in settingthreshold or for classification. We choose3as the wavelet decomposition scale for extractingsand land and waste grassland, while use2for extracting dry land, shrubbery, grassland andother LUCC types.(2) Due to the marked differences of spectrum between target class and background inimages of CRI, CWI, and CSI index, the slight linear or area object of target class havingweak spectrum and subject to interference of other adjacent ground objects, such as thin river,road, and tiny residential area, can be able to recognition certainly. Combination of thresholdsegmentation and mask operation on CRI, CWI, and CSI images can retain the entire targetclass while separates background interference information from it quickly. Then furtherclassification to threshold segmentation can make the recognition more simply, distinguishobjects with same spectrum certainly, and improve the mapping accuracy.(3) LBV transformation can enhance spectral features of irrigable land, dry land,woodland, shrubbery, grassland and other vegetation type effectivly. Meanwhie it candecrease subtle spectral difference in large patchess. Homogeneity is improved and edgefeatures is also enhanced. Thus LBV transformation provides conditions for exactly choosingsegmentation scale in object-oriented method.(4) After object-oriented image segmentation, basic unit of remot sensing image is notthe pixel, but homogeneity object formed by adjacent and the same class type pixels.Classificating for object can make the selection of the training samples and setting clustering centers easier. In addition, it is favorable to decrease salt and pepper effect in classificationresults. Thus the rationality and applicability about image classify are improved. In theprocess of identifying waters, residential area, irrigable land, dry land, shrubbery, andwoodland, segmentation scale (SC) is confirmed by choosing the classic training samplingareas form multiple positions of the whole imagery, and the segmentation scales are6.2,9.0,7.0,4.2and5.3respectively.(5) The overall accuracy of support vector machine classification is much better than thatof minimum distance, maximum likelihood and BP artificial neural net, as well as betterintegrity and continuity of patches. Morphological bandpass filter is constructed by usingmorphological open-close operation, and it has a good applicability on optimizing binaryimage of classification results. In post classification phase, open-close operation ofmathematical morphology can filter noise speckles, connect broken lines, fill the holes inorder to improve precision of image classify, meanwhile keeping original shape of largpatches. The operating process of open-close operation has high calculating efficiency,especially for road, residential area and waters extraction.(6) In1986, grassland, unusable land, cultivated land, forest land, waters, residential areain Hengshan County covered141185.61hm~2,128043.90hm~2,87037.38hm~2,61474.77hm~2,5687.82hm~2,137.70hm~2respectively, which were account for33.33%,30.23%,20.55%,14.51%,1.34%,0.03%of the whole area of the county.56.08%of unusable landis sandy land;81.24%of cultivated land is dry land;84.90%of forest land is shrubbery. In2003, grassland, forest land, cultivated land, unusable land, waters, residential area, roadHengshan County covered181424.88hm~2,84919.95hm~2,80475.30hm~2,72379.44hm~2,3747.51hm~2,394.38hm~2,225.72hm~2respectively, which were account for42.83%,20.05%,19.00%,17.09%,0.89%,0.09%,0.05%of the whole area of the county.88.15%offorest land is shrubbery;72.73%of cultivated land is dry land;42.06%of unusable land issandy land.(7) In the period of1986to2003, the coverage area proportion of sandy land, wastegrassland, wters, dry land had certain reduction. Transfer matrix calculations indicated that:the decreased sandy land converted mainly to grassland, waster grassland, which wereaccount for26.96%,23.56%of sandy land area in1986respectively; the decreased wastergrassland converted mainly to grassland, shrubbery, which were account for41.53%,8.21%of waster grassland area in1986respectively; the decreased waters converted mainly toirrigable land, which were account for28.42%of waters area in1986; the decreased dry landconverted mainly to grassland, shrubbery, which were account for14.22%,5.74%of dry land area in1986respectively. From1986to2003, the coverage area proportion of irrigableland, shrubbery, woodland, residential area had certain increase. Transfer matrix calculationsindicate that: the increased irrigable land is converted mainly by shrubbery, waters, grassland,which were account for8.02%,7.36%,5.00%of the irrigable land area in2003respectively;the increased shrubbery is converted mainly by grassland, waster grassland, which wereaccount for13.18%,6.17%of the shurbbery area in2003respectively; the increasedwoodland is converted mainly by grassland, dry land, which were account for15.22%,12.58%of the woodland area in2003respectively; the increased grassland is converted mainly bywaster grassland, sandy land, which were account for12.88%,10.67%of the grassland areain2003respectively; the increased residential area is converted mainly by irrigable land, dryland, which were account for31.36%,10.22%of the residential area in2003respectively.